--------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  E:\REStat_MS14767_Vol96(2)\Estimation\REStat_MS14797_Vol96(2)_estimation_compustat.log
  log type:  text
 opened on:  20 Dec 2014, 20:30:40

. 
. 
. ********************************************************************************
. ********                                                ************************
. ********      in this file we do the main regressions   ************************
. ********                                                ************************
. ********************************************************************************
. 
. use RESTATestimation_sample_final.dta, clear

. 
. * we drop one industry (59 observations + 1 obs) which has a wierd pattern
. * we also drop industry 9997 ("industrial conglomerates") following th suggestion of one referee
. * we finally drop industries with less than 4 firms
. 
. gen drop=0

. replace drop=1 if SIC4==3241
(59 real changes made)

. replace drop=1 if SIC4==9997
(103 real changes made)

. replace drop=1 if ncomp<4
(1927 real changes made)

. *replace drop=1 if network==1 
. *drop if drop==1
. 
. xtset firmnum year
       panel variable:  firmnum (unbalanced)
        time variable:  year, 1986 to 1999, but with gaps
                delta:  1 unit

. 
. 
. *************************************************************
. **** preliminary statistics - Table 1
. *************************************************************
. gen RJVhorMS_s=0

. replace RJVhorMS_s=1 if networkMS>0 & networkMS<.1668186
(1215 real changes made)

. 
. gen RJVhorMS_m=0

. replace RJVhorMS_m=1 if networkMS>=.1668186 & networkMS<.5327271
(1623 real changes made)

. 
. gen RJVhorMS_l=0

. replace RJVhorMS_l=1 if networkMS>=.5327271
(537 real changes made)

. 
. tabstat MS sales_new ta rd2 rd_int patents network networkMS coverage RJVsametot links2_ver m_rdexpense  m_lmkval2 ncomp if RJ
> V2==0 & zero_same==1, stat (mean min max sd n) c(st)

    variable |      mean       min       max        sd         N
-------------+--------------------------------------------------
          MS |  .0730385         0         1  .1557429     59996
   sales_new |  555.8397         0     76431  2206.155     60001
          ta |  1118.282         0    716937  9330.385     60001
         rd2 |  2.593199         0      1667  32.03382     60001
      rd_int |  .3989048         0      1910   14.7583     58274
     patents |  3.804498         0  5560.544   85.3941     60001
     network |         0         0         0         0     60001
   networkMS |         0         0         0         0     60001
    coverage |         0         0         0         0     60001
  RJVsametot |         0         0         0         0       995
  links2_ver |         0         0         0         0     60001
 m_rdexpense |  6.644512         0    962.55  28.22298     51663
   m_lmkval2 |  3.239057  -2.56395  9.790785  1.126031     60001
       ncomp |  43.93717         1       272  57.45334     60001
----------------------------------------------------------------

. tabstat MS sales_new ta rd2 rd_int patents network networkMS coverage RJVsametot links2_ver m_rdexpense  m_lmkval2 ncomp if RJ
> V2==1, stat (mean min max sd n) c(st)

    variable |      mean       min       max        sd         N
-------------+--------------------------------------------------
          MS |  .1491152         0         1  .2182085      5987
   sales_new |   4837.26         0    168919  12726.29      5987
          ta |  8679.858         0    405200  29974.74      5987
         rd2 |   144.125         0      8900  548.0336      5987
      rd_int |   .961145         0      3309  43.40054      5969
     patents |  150.8789         0  10410.63  523.1952      5987
     network |  .0893646         0         1  .1602426      5987
   networkMS |  .1700714         0  .9807433   .229673      5987
    coverage |  .1076865         0         1  .1386854      5987
  RJVsametot |  2.605312         0       167  8.342689      5987
  links2_ver |  50.27459        -1       691  69.95451      5987
 m_rdexpense |  68.60716         0  2518.722  193.0308      5472
   m_lmkval2 |  3.881523         0  10.34555   1.04423      5987
       ncomp |   79.7538         1       547  116.4858      5987
----------------------------------------------------------------

. tabstat MS sales_new ta rd2 rd_int patents network networkMS coverage RJVsametot links2_ver m_rdexpense  m_lmkval2 ncomp if RJ
> Vver==1, stat (mean min max sd n) c(st)

    variable |      mean       min       max        sd         N
-------------+--------------------------------------------------
          MS |  .2267718         0         1  .2643165      2366
   sales_new |  3909.477         0    137634  8734.257      2366
          ta |  6825.396      .413    386684  24392.04      2366
         rd2 |  70.55779         0      3867  234.1988      2366
      rd_int |  .1990466         0  127.0652  2.964175      2363
     patents |  124.7769         0  5380.281  422.3369      2366
     network |         0         0         0         0      2366
   networkMS |         0         0         0         0      2366
    coverage |         0         0         0         0      2366
  RJVsametot |  .4289941         0        27  2.485439      2366
  links2_ver |  31.51352         0       473  46.46614      2366
 m_rdexpense |  36.47952         0      1315  104.6382      2221
   m_lmkval2 |  3.830766         0  10.34555  1.181251      2366
       ncomp |  35.99239         1       523  59.76811      2366
----------------------------------------------------------------

. tabstat MS sales_new ta rd2 rd_int patents network networkMS coverage RJVsametot links2_ver m_rdexpense  m_lmkval2 ncomp if RJ
> Vhor==1, stat (mean min max sd n) c(st)

    variable |      mean       min       max        sd         N
-------------+--------------------------------------------------
          MS |  .0983735         0         1  .1630216      3621
   sales_new |  5443.483         0    168919  14732.39      3621
          ta |  9891.584         0    405200  33065.19      3621
         rd2 |  192.1945         0      8900  674.5062      3621
      rd_int |  1.460546         0      3309  55.78426      3606
     patents |  167.9342         0  10410.63  579.1164      3621
     network |  .1477563  .0018315         1    .18393      3621
   networkMS |  .2811979         0  .9807433  .2365782      3621
    coverage |    .17805  .0078895         1  .1388268      3621
  RJVsametot |  4.027341         1       167  10.29255      3621
  links2_ver |  62.53328        -1       691  79.37965      3621
 m_rdexpense |  90.55594         0  2518.722  232.5039      3251
   m_lmkval2 |  3.914689  1.192847  7.818902  .9427151      3621
       ncomp |   108.348         1       547  134.2914      3621
----------------------------------------------------------------

. 
. 
. tabstat MS  sales_new ta rd2 rd_int patents network coverage RJVsametot links2_ver m_rdexpense  m_lmkval2 ncomp if RJVhor==1 &
>  (network==0 | network<.031746), stat (mean min max sd n) c(st)

    variable |      mean       min       max        sd         N
-------------+--------------------------------------------------
          MS |  .0431957         0  .9029148   .098841       905
   sales_new |  3197.533         0     42848  6298.334       905
          ta |  12985.79         0    388570  45099.55       905
         rd2 |  97.88425         0      2600  291.1504       905
      rd_int |  4.858841         0      3309  111.4433       896
     patents |  92.59804         0  2700.003  303.9908       905
     network |  .0173695  .0018315  .0314465  .0079704       905
    coverage |  .0958235  .0078895  .4727273   .063122       905
  RJVsametot |  1.667403         1        21  2.158767       905
  links2_ver |  36.68508         0       296  40.13191       905
 m_rdexpense |  42.07423         0  454.0002  57.56249       795
   m_lmkval2 |  3.527935  1.829307  6.195434  .7509241       905
       ncomp |  216.9613        33       547  169.3779       905
----------------------------------------------------------------

. tabstat MS  sales_new ta rd2 rd_int patents network coverage RJVsametot links2_ver m_rdexpense  m_lmkval2 ncomp if RJVhor==1 &
>  (network==0 | network>.031746 & network<.1948052),  stat (mean min max sd n) c(st)

    variable |      mean       min       max        sd         N
-------------+--------------------------------------------------
          MS |  .0949567         0  .9044477  .1539917      1811
   sales_new |  3611.018         0     81667  7163.081      1811
          ta |  5251.369         0     92473  10273.96      1811
         rd2 |  145.7234         0      5227  522.6698      1811
      rd_int |   .211578         0       157  3.755589      1807
     patents |  170.6768         0  10410.63   651.223      1811
     network |    .09184   .031746  .1948052   .046767      1811
    coverage |    .14991  .0102041  .5714286  .0833022      1811
  RJVsametot |   2.71894         1        71  4.596656      1811
  links2_ver |  60.35892        -1       691  70.07799      1811
 m_rdexpense |  65.85208         0  2518.722  181.6016      1609
   m_lmkval2 |  3.801582  1.821925  6.396855  .8693246      1811
       ncomp |  92.14578         7       523  110.8544      1811
----------------------------------------------------------------

. tabstat MS  sales_new ta rd2 rd_int patents network coverage RJVsametot links2_ver m_rdexpense  m_lmkval2 ncomp if RJVhor==1 &
>  (network==0 | network>.1948052), stat (mean min max sd n) c(st)

    variable |      mean       min       max        sd         N
-------------+--------------------------------------------------
          MS |  .1603889         0         1  .2055515       905
   sales_new |  11356.39         0    168919  26075.91       905
          ta |  16082.94         0    405200  45184.72       905
         rd2 |  379.4984         0      8900  1068.529       905
      rd_int |  .5879124         0  158.4895  8.387736       903
     patents |  237.7822         0  4509.024  625.1998       905
     network |  .3900376  .1973684         1  .2213137       905
    coverage |  .3165876  .0789474         1  .1781281       905
  RJVsametot |  9.005525         1       167  18.53199       905
  links2_ver |   92.7326        -1       632  110.4435       905
 m_rdexpense |  182.9898      .597  2518.722  360.4895       847
   m_lmkval2 |   4.52778  1.192847  7.818902  .9644175       905
       ncomp |  32.15691         1       153  27.85165       905
----------------------------------------------------------------

. 
. tabstat networkMS if RJVhor==1 & (networkMS==0 | networkMS<.1059457), stat (mean min max sd n) c(st)

    variable |      mean       min       max        sd         N
-------------+--------------------------------------------------
   networkMS |  .0334357         0  .1059457  .0330585      1090
----------------------------------------------------------------

. tabstat networkMS if networkMS>=.1059457 & networkMS<.4082499, stat (mean min max sd n) c(st)

    variable |      mean       min       max        sd         N
-------------+--------------------------------------------------
   networkMS |  .2523314  .1067712  .4078751  .0905292      1526
----------------------------------------------------------------

. tabstat networkMS if networkMS>=.4082499, stat (mean min max sd n) c(st)

    variable |      mean       min       max        sd         N
-------------+--------------------------------------------------
   networkMS |  .5937461  .4084549  .9807433  .1499107      1005
----------------------------------------------------------------

. 
. 
. gen aa=1 if network>0 & network<.031746
(111704 missing values generated)

. replace aa=0 if network>.031746 & network<.1948052
(1811 real changes made)

. ttest MS, by(aa)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |    1811    .0949567    .0036186    .1539917    .0878596    .1020537
       1 |     905    .0431957    .0032856     .098841    .0367474    .0496439
---------+--------------------------------------------------------------------
combined |    2716    .0777094    .0026903    .1402047    .0724342    .0829846
---------+--------------------------------------------------------------------
    diff |             .051761    .0056214                .0407383    .0627836
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   9.2079
Ho: diff = 0                                     degrees of freedom =     2714

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

. gen bb=1 if aa==0
(110798 missing values generated)

. replace bb=0 if network>.1948052 & network!=.
(905 real changes made)

. ttest MS, by(bb)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     905    .1603889    .0068328    .2055515     .146979    .1737988
       1 |    1811    .0949567    .0036186    .1539917    .0878596    .1020537
---------+--------------------------------------------------------------------
combined |    2716    .1167594    .0033691    .1755813    .1101531    .1233656
---------+--------------------------------------------------------------------
    diff |            .0654322    .0070377                .0516324     .079232
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   9.2974
Ho: diff = 0                                     degrees of freedom =     2714

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

. drop aa bb

. 
. 
. ************************
. ***** UNIT ROOTS TESTS
. ************************
. 
. egen a=count(firmnum), by(firmnum)

. 
. *xtunitroot fisher MS if zero_same==0 & drop==0 & a>3, pperron lags(1)
. *xtunitroot fisher MS if zero_same==0 & drop==0 & a>3, dfuller lags(1)
. 
. 
. ************************
. ***** Determinants of RJV participation - Table 3
. ************************
. 
. *we first define the estimation sample
. xtdpd L(0/1).MS L(0/2).RJV2 L(0/2).lrd2 L(1/1).m_lmkval L(1/1).m_lrd dy2-dy13 if zero_same==0 & drop==0 &  network>=0, dgmmiv(
> MS, lag(2 5)) dgmmiv(RJV, lag(3 3)) dgmmiv(lrd2, lag(2 6))  lgmmiv(l(5/5).MS l(6/6).lrd2)  div(l(1/1).patents_t_2 L(1/2).ta) d
> iv(L(1/2).m_lmkval L(1/2).m_lrd dy2-dy13) twostep
note: dy2 dropped from div() because of collinearity
note: dy2 dropped because of collinearity
note: D.dy3 dropped because of collinearity

Dynamic panel-data estimation                Number of obs         =     36485
Group variable: firmnum                      Number of groups      =      5785
Time variable: year
                                             Obs per group:    min =         1
                                                               avg =  6.306828
                                                               max =        12

Number of instruments =    133               Wald chi2(19)         =   4999.00
                                             Prob > chi2           =    0.0000
Two-step results
------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          MS |
         L1. |   .9004759   .0146077    61.64   0.000     .8718453    .9291064
             |
        RJV2 |
         --. |   .0199117   .0102792     1.94   0.053    -.0002352    .0400586
         L1. |  -.0162311   .0106315    -1.53   0.127    -.0370685    .0046063
         L2. |  -.0060569   .0083527    -0.73   0.468    -.0224278    .0103141
             |
        lrd2 |
         --. |   .0040635    .001529     2.66   0.008     .0010667    .0070604
         L1. |   -.002751   .0014694    -1.87   0.061    -.0056309    .0001289
         L2. |  -.0007963   .0003357    -2.37   0.018    -.0014542   -.0001384
             |
    m_lmkval |
         L1. |  -.0002435    .000382    -0.64   0.524    -.0009923    .0005052
             |
       m_lrd |
         L1. |   .0031887   .0018256     1.75   0.081    -.0003894    .0067667
             |
         dy4 |  -.0000399   .0002847    -0.14   0.889    -.0005978    .0005181
         dy5 |  -.0003679   .0002863    -1.29   0.199    -.0009291    .0001932
         dy6 |  -.0003463   .0003093    -1.12   0.263    -.0009526    .0002599
         dy7 |   -.000228   .0002911    -0.78   0.433    -.0007986    .0003426
         dy8 |  -.0005561   .0002996    -1.86   0.063    -.0011432     .000031
         dy9 |  -.0003924   .0003152    -1.24   0.213    -.0010101    .0002254
        dy10 |  -.0006663   .0002975    -2.24   0.025    -.0012494   -.0000831
        dy11 |  -.0003996   .0002994    -1.33   0.182    -.0009864    .0001873
        dy12 |  -.0003021    .000283    -1.07   0.286    -.0008567    .0002526
        dy13 |  -.0003855    .000299    -1.29   0.197    -.0009715    .0002004
       _cons |  -.0002744    .001658    -0.17   0.869    -.0035241    .0029753
------------------------------------------------------------------------------
Warning: gmm two-step standard errors are biased; robust standard 
         errors are recommended.
Instruments for differenced equation
        GMM-type: L(2/5).MS L(3/3).RJV L(2/6).lrd2
        Standard: LD.patents_t_2 LD.ta L2D.ta LD.m_lmkval L2D.m_lmkval
                  LD.m_lrd L2D.m_lrd D.dy3 D.dy4 D.dy5 D.dy6 D.dy7 D.dy8
                  D.dy9 D.dy10 D.dy11 D.dy12 D.dy13
Instruments for level equation
        GMM-type: L6D.MS L7D.lrd2
        Standard: _cons

. 
. xtprobit L(0/0).RJV2 L(3/3).MS patents_t_3 l(3/3).lta L(3/3).lrd2 L(1/1).m_lrd L(2/2).m_lmkval dy* if e(sample)
note: dy1 dropped because of collinearity
note: dy2 dropped because of collinearity
note: dy3 dropped because of collinearity
note: dy5 dropped because of collinearity

Fitting comparison model:

Iteration 0:   log likelihood = -11366.996
Iteration 1:   log likelihood = -8629.3625
Iteration 2:   log likelihood = -8528.7834
Iteration 3:   log likelihood = -8526.7808
Iteration 4:   log likelihood = -8526.7778

Fitting full model:

rho =  0.0     log likelihood = -8526.7778
rho =  0.1     log likelihood = -6954.0815
rho =  0.2     log likelihood = -6197.8665
rho =  0.3     log likelihood = -5743.1497
rho =  0.4     log likelihood = -5444.6552
rho =  0.5     log likelihood = -5243.8571
rho =  0.6     log likelihood =  -5116.573
rho =  0.7     log likelihood = -5058.2953
rho =  0.8     log likelihood = -5086.4417

Iteration 0:   log likelihood = -5046.5145  
Iteration 1:   log likelihood = -4635.9025  
Iteration 2:   log likelihood = -4409.7024  (not concave)
Iteration 3:   log likelihood = -3543.1988  (not concave)
Iteration 4:   log likelihood = -3535.5537  (not concave)
Iteration 5:   log likelihood = -3503.2843  
Iteration 6:   log likelihood = -3502.3277  (not concave)
Iteration 7:   log likelihood = -3492.3295  
Iteration 8:   log likelihood = -3492.3295  (backed up)
Iteration 9:   log likelihood = -3482.3786  
Iteration 10:  log likelihood = -3479.5096  
Iteration 11:  log likelihood = -3479.4452  
Iteration 12:  log likelihood =  -3479.445  

Random-effects probit regression                Number of obs      =     30618
Group variable: firmnum                         Number of groups   =      5169

Random effects u_i ~ Gaussian                   Obs per group: min =         1
                                                               avg =       5.9
                                                               max =        11

Integration method: mvaghermite                 Integration points =        12

                                                Wald chi2(16)      =   1181.10
Log likelihood  =  -3479.445                    Prob > chi2        =    0.0000

------------------------------------------------------------------------------
        RJV2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          MS |
         L3. |   .0404168    .631363     0.06   0.949    -1.197032    1.277866
             |
 patents_t_3 |   .0038014   .0003474    10.94   0.000     .0031206    .0044823
             |
         lta |
         L3. |   .5189765   .0435781    11.91   0.000      .433565     .604388
             |
        lrd2 |
         L3. |   .4263255   .0491828     8.67   0.000     .3299289    .5227221
             |
       m_lrd |
         L1. |   .9851445   .1020281     9.66   0.000     .7851731    1.185116
             |
    m_lmkval |
         L2. |  -.1316375    .062586    -2.10   0.035    -.2543038   -.0089712
             |
         dy4 |  -.3941421   .1394577    -2.83   0.005    -.6674742     -.12081
         dy6 |   .2680991   .1312046     2.04   0.041     .0109428    .5252554
         dy7 |   .3265212   .1326518     2.46   0.014     .0665284    .5865139
         dy8 |   .7080632   .1345693     5.26   0.000     .4443123    .9718141
         dy9 |    .911376   .1373374     6.64   0.000     .6421997    1.180552
        dy10 |   1.204573   .1404034     8.58   0.000     .9293878    1.479759
        dy11 |   1.361686   .1392142     9.78   0.000     1.088831     1.63454
        dy12 |   1.477424   .1405313    10.51   0.000     1.201988    1.752861
        dy13 |   1.388507   .1417227     9.80   0.000     1.110736    1.666279
        dy14 |   1.702114   .1455027    11.70   0.000     1.416934    1.987294
       _cons |  -10.59011   .3438684   -30.80   0.000    -11.26408   -9.916145
-------------+----------------------------------------------------------------
    /lnsig2u |   3.021938   .0422419                      2.939146    3.104731
-------------+----------------------------------------------------------------
     sigma_u |    4.53112   .0957016                      4.347378    4.722628
         rho |   .9535554   .0018708                       .949748    .9570875
------------------------------------------------------------------------------
Likelihood-ratio test of rho=0: chibar2(01) =  1.0e+04 Prob >= chibar2 = 0.000

. eststo RJV1p

. xtprobit L(0/0).RJVver L(3/3).MS patents_t_3 l(3/3).lta L(3/3).lrd2 L(1/1).m_lrd L(2/2).m_lmkval dy*  if e(sample)
note: dy1 dropped because of collinearity
note: dy2 dropped because of collinearity
note: dy3 dropped because of collinearity
note: dy5 dropped because of collinearity

Fitting comparison model:

Iteration 0:   log likelihood = -3859.6602
Iteration 1:   log likelihood = -3493.7333
Iteration 2:   log likelihood = -3419.9933
Iteration 3:   log likelihood = -3419.6763
Iteration 4:   log likelihood = -3419.6763

Fitting full model:

rho =  0.0     log likelihood = -3419.6763
rho =  0.1     log likelihood = -2867.3703
rho =  0.2     log likelihood = -2662.0212
rho =  0.3     log likelihood = -2593.0152
rho =  0.4     log likelihood = -2599.0989

Iteration 0:   log likelihood = -2592.9088  
Iteration 1:   log likelihood = -1987.6601  
Iteration 2:   log likelihood = -1867.7205  
Iteration 3:   log likelihood = -1836.0961  (not concave)
Iteration 4:   log likelihood = -1821.8853  (not concave)
Iteration 5:   log likelihood = -1821.2131  (not concave)
Iteration 6:   log likelihood = -1818.9119  (not concave)
Iteration 7:   log likelihood = -1818.7211  (not concave)
Iteration 8:   log likelihood = -1818.5678  (not concave)
Iteration 9:   log likelihood = -1818.2215  (not concave)
Iteration 10:  log likelihood = -1818.2067  (not concave)
Iteration 11:  log likelihood = -1818.2057  (not concave)
Iteration 12:  log likelihood = -1815.5805  (not concave)
Iteration 13:  log likelihood = -1812.9538  (not concave)
Iteration 14:  log likelihood = -1811.2021  
Iteration 15:  log likelihood = -1807.0946  
Iteration 16:  log likelihood = -1803.3462  
Iteration 17:  log likelihood = -1803.3195  
Iteration 18:  log likelihood = -1803.3195  

Random-effects probit regression                Number of obs      =     30618
Group variable: firmnum                         Number of groups   =      5169

Random effects u_i ~ Gaussian                   Obs per group: min =         1
                                                               avg =       5.9
                                                               max =        11

Integration method: mvaghermite                 Integration points =        12

                                                Wald chi2(16)      =    228.21
Log likelihood  = -1803.3195                    Prob > chi2        =    0.0000

------------------------------------------------------------------------------
      RJVver |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          MS |
         L3. |   3.391798   .5612128     6.04   0.000     2.291841    4.491755
             |
 patents_t_3 |  -.0009444   .0003306    -2.86   0.004    -.0015924   -.0002964
             |
         lta |
         L3. |   .1632265   .0340924     4.79   0.000     .0964066    .2300464
             |
        lrd2 |
         L3. |   .1229828   .0490011     2.51   0.012     .0269424    .2190233
             |
       m_lrd |
         L1. |    .287807   .0877689     3.28   0.001     .1157831     .459831
             |
    m_lmkval |
         L2. |  -.1302063   .0599957    -2.17   0.030    -.2477957   -.0126168
             |
         dy4 |  -.2430067   .1594553    -1.52   0.128    -.5555334    .0695199
         dy6 |   -.130684   .1542592    -0.85   0.397    -.4330264    .1716585
         dy7 |  -.2888477   .1592933    -1.81   0.070    -.6010568    .0233614
         dy8 |   .0286808   .1517619     0.19   0.850    -.2687669    .3261286
         dy9 |   .1369109   .1497109     0.91   0.360    -.1565171    .4303389
        dy10 |    .296022   .1491915     1.98   0.047     .0036121    .5884319
        dy11 |   .2465103   .1469893     1.68   0.094    -.0415835     .534604
        dy12 |   .3542502   .1440711     2.46   0.014     .0718761    .6366243
        dy13 |   .3341063   .1446461     2.31   0.021     .0506051    .6176075
        dy14 |   .4220427   .1449008     2.91   0.004     .1380424     .706043
       _cons |  -5.908265   .2578678   -22.91   0.000    -6.413676   -5.402853
-------------+----------------------------------------------------------------
    /lnsig2u |   1.599919     .04552                      1.510702    1.689137
-------------+----------------------------------------------------------------
     sigma_u |   2.225451   .0506513                      2.128358    2.326973
         rho |   .8320071   .0063624                      .8191652    .8441106
------------------------------------------------------------------------------
Likelihood-ratio test of rho=0: chibar2(01) =  3232.71 Prob >= chibar2 = 0.000

. eststo RJV2p

. xtprobit L(0/0).RJVhor L(3/3).MS  patents_t_3 l(3/3).lta L(3/3).lrd2 L(1/1).m_lrd L(2/2).m_lmkval dy* if e(sample)
note: dy1 omitted because of collinearity
note: dy2 omitted because of collinearity
note: dy3 omitted because of collinearity
note: dy14 omitted because of collinearity

Fitting comparison model:

Iteration 0:   log likelihood = -9588.5374  
Iteration 1:   log likelihood = -7437.7798  
Iteration 2:   log likelihood = -7345.9661  
Iteration 3:   log likelihood = -7345.8343  
Iteration 4:   log likelihood = -7345.8343  

Fitting full model:

rho =  0.0     log likelihood = -7345.8343
rho =  0.1     log likelihood = -6033.4632
rho =  0.2     log likelihood = -5412.8739
rho =  0.3     log likelihood = -5051.4875
rho =  0.4     log likelihood = -4825.9831
rho =  0.5     log likelihood = -4687.0573
rho =  0.6     log likelihood = -4615.6322
rho =  0.7     log likelihood = -4614.7849
rho =  0.8     log likelihood = -4703.4415

Iteration 0:   log likelihood = -4601.5906  
Iteration 1:   log likelihood = -4024.5344  
Iteration 2:   log likelihood = -3471.8454  
Iteration 3:   log likelihood = -3363.1356  
Iteration 4:   log likelihood = -3247.7907  
Iteration 5:   log likelihood = -3241.6563  
Iteration 6:   log likelihood = -3241.1119  
Iteration 7:   log likelihood = -3241.0309  
Iteration 8:   log likelihood = -3240.9999  
Iteration 9:   log likelihood = -3240.9816  
Iteration 10:  log likelihood = -3240.9649  
Iteration 11:  log likelihood = -3240.9642  
Iteration 12:  log likelihood =  -3236.863  
Iteration 13:  log likelihood = -3236.8211  
Iteration 14:  log likelihood = -3236.8207  
Iteration 15:  log likelihood = -3236.8207  

Random-effects probit regression                Number of obs      =     30618
Group variable: firmnum                         Number of groups   =      5169

Random effects u_i ~ Gaussian                   Obs per group: min =         1
                                                               avg =       5.9
                                                               max =        11

Integration method: mvaghermite                 Integration points =        12

                                                Wald chi2(16)      =    497.16
Log likelihood  = -3236.8207                    Prob > chi2        =    0.0000

------------------------------------------------------------------------------
      RJVhor |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          MS |
         L3. |  -1.819329   .6998278    -2.60   0.009    -3.190967    -.447692
             |
 patents_t_3 |   .0017909   .0005645     3.17   0.002     .0006845    .0028974
             |
         lta |
         L3. |   .5218946   .0654276     7.98   0.000     .3936588    .6501303
             |
        lrd2 |
         L3. |   .3883138   .0474287     8.19   0.000     .2953552    .4812724
             |
       m_lrd |
         L1. |   .9266491   .1392729     6.65   0.000     .6536792    1.199619
             |
    m_lmkval |
         L2. |  -.1516196   .0600819    -2.52   0.012    -.2693779   -.0338614
             |
         dy1 |          0  (omitted)
         dy2 |          0  (omitted)
         dy3 |          0  (omitted)
         dy4 |  -1.536709   .1475216   -10.42   0.000    -1.825846   -1.247572
         dy5 |  -1.289876   .1359316    -9.49   0.000    -1.556297   -1.023455
         dy6 |  -.9693023   .1297147    -7.47   0.000    -1.223538   -.7150661
         dy7 |   -.875714   .1303177    -6.72   0.000    -1.131132   -.6202961
         dy8 |  -.6846442   .1234731    -5.54   0.000     -.926647   -.4426414
         dy9 |  -.5454838   .1192677    -4.57   0.000    -.7792441   -.3117235
        dy10 |  -.3391488   .1147373    -2.96   0.003    -.5640298   -.1142677
        dy11 |   -.173752   .1096239    -1.58   0.113     -.388611    .0411069
        dy12 |  -.1636969   .1043478    -1.57   0.117    -.3682149    .0408211
        dy13 |  -.2515498   .0980822    -2.56   0.010    -.4437875   -.0593121
        dy14 |          0  (omitted)
       _cons |  -7.867171   .5627941   -13.98   0.000    -8.970227   -6.764115
-------------+----------------------------------------------------------------
    /lnsig2u |   2.434661    .072242                      2.293069    2.576253
-------------+----------------------------------------------------------------
     sigma_u |   3.378158   .1220224                      3.147268    3.625986
         rho |   .9194325   .0053514                      .9083014    .9293175
------------------------------------------------------------------------------
Likelihood-ratio test of rho=0: chibar2(01) =  8218.03 Prob >= chibar2 = 0.000

. eststo RJV3p

. xtprobit L(0/0).RJVhor_s L(3/3).MS  L(3/3).MS patents_t_3 l(3/3).lta L(3/3).lrd2 L(1/1).m_lrd L(2/2).m_lmkval dy* if e(sample)
note: L3.MS omitted because of collinearity
note: dy1 omitted because of collinearity
note: dy2 omitted because of collinearity
note: dy3 omitted because of collinearity
note: dy14 omitted because of collinearity

Fitting comparison model:

Iteration 0:   log likelihood = -3261.3227  
Iteration 1:   log likelihood = -2874.4232  
Iteration 2:   log likelihood = -2830.6506  
Iteration 3:   log likelihood =  -2830.443  
Iteration 4:   log likelihood =  -2830.443  

Fitting full model:

rho =  0.0     log likelihood =  -2830.443
rho =  0.1     log likelihood = -2454.1225
rho =  0.2     log likelihood = -2307.4433
rho =  0.3     log likelihood = -2261.4184
rho =  0.4     log likelihood = -2277.2272

Iteration 0:   log likelihood = -2261.4218  
Iteration 1:   log likelihood = -1797.2777  
Iteration 2:   log likelihood = -1650.8262  (not concave)
Iteration 3:   log likelihood = -1643.2227  (not concave)
Iteration 4:   log likelihood = -1639.4003  (not concave)
Iteration 5:   log likelihood = -1638.7557  (not concave)
Iteration 6:   log likelihood = -1638.5343  (not concave)
Iteration 7:   log likelihood = -1637.9261  (not concave)
Iteration 8:   log likelihood = -1637.3245  (not concave)
Iteration 9:   log likelihood = -1637.2682  (not concave)
Iteration 10:  log likelihood = -1637.2681  (not concave)
Iteration 11:  log likelihood = -1637.1333  (not concave)
Iteration 12:  log likelihood = -1637.0665  (not concave)
Iteration 13:  log likelihood = -1636.9452  (not concave)
Iteration 14:  log likelihood = -1636.7193  (not concave)
Iteration 15:  log likelihood = -1636.4128  (not concave)
Iteration 16:  log likelihood = -1636.0042  (not concave)
Iteration 17:  log likelihood =  -1635.628  
Iteration 18:  log likelihood =   -1634.24  
Iteration 19:  log likelihood = -1633.9783  
Iteration 20:  log likelihood =  -1633.978  
Iteration 21:  log likelihood =  -1633.978  

Random-effects probit regression                Number of obs      =     30618
Group variable: firmnum                         Number of groups   =      5169

Random effects u_i ~ Gaussian                   Obs per group: min =         1
                                                               avg =       5.9
                                                               max =        11

Integration method: mvaghermite                 Integration points =        12

                                                Wald chi2(16)      =    249.86
Log likelihood  =  -1633.978                    Prob > chi2        =    0.0000

------------------------------------------------------------------------------
    RJVhor_s |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          MS |
         L3. |  -1.903607   .7692286    -2.47   0.013    -3.411268    -.395947
         L3. |          0  (omitted)
             |
 patents_t_3 |  -.0006135   .0002942    -2.09   0.037    -.0011901   -.0000369
             |
         lta |
         L3. |   .2533596   .0368952     6.87   0.000     .1810464    .3256728
             |
        lrd2 |
         L3. |    .238455   .0516518     4.62   0.000     .1372193    .3396906
             |
       m_lrd |
         L1. |   .1568461   .1059187     1.48   0.139    -.0507507     .364443
             |
    m_lmkval |
         L2. |  -.5797672   .0813437    -7.13   0.000    -.7391979   -.4203365
             |
         dy1 |          0  (omitted)
         dy2 |          0  (omitted)
         dy3 |          0  (omitted)
         dy4 |  -1.521775    .200194    -7.60   0.000    -1.914148   -1.129402
         dy5 |  -1.420743    .186489    -7.62   0.000    -1.786255   -1.055232
         dy6 |   -1.08348   .1697434    -6.38   0.000     -1.41617   -.7507886
         dy7 |  -1.541071   .1885093    -8.18   0.000    -1.910542   -1.171599
         dy8 |  -1.122819   .1663344    -6.75   0.000    -1.448829   -.7968099
         dy9 |  -.7709661   .1523337    -5.06   0.000    -1.069535   -.4723976
        dy10 |  -.4110645   .1398304    -2.94   0.003     -.685127   -.1370019
        dy11 |  -.2761263   .1279209    -2.16   0.031    -.5268468   -.0254059
        dy12 |  -.1647933   .1214971    -1.36   0.175    -.4029233    .0733366
        dy13 |  -.1255687    .113661    -1.10   0.269    -.3483402    .0972027
        dy14 |          0  (omitted)
       _cons |  -3.316162   .3259078   -10.18   0.000     -3.95493   -2.677395
-------------+----------------------------------------------------------------
    /lnsig2u |   1.513694   .0590754                      1.397908     1.62948
-------------+----------------------------------------------------------------
     sigma_u |   2.131545    .062961                      2.011648    2.258588
         rho |    .819608   .0087343                      .8018518    .8360984
------------------------------------------------------------------------------
Likelihood-ratio test of rho=0: chibar2(01) =  2392.93 Prob >= chibar2 = 0.000

. eststo RJV4p

. xtprobit L(0/0).RJVhor_m L(3/3).MS  L(3/3).MS patents_t_3 l(3/3).lta L(3/3).lrd2 L(1/1).m_lrd L(2/2).m_lmkval dy* if e(sample)
note: L3.MS omitted because of collinearity
note: dy1 omitted because of collinearity
note: dy2 omitted because of collinearity
note: dy3 omitted because of collinearity
note: dy14 omitted because of collinearity

Fitting comparison model:

Iteration 0:   log likelihood = -5951.1849  
Iteration 1:   log likelihood = -5104.3358  
Iteration 2:   log likelihood = -5039.0019  
Iteration 3:   log likelihood = -5038.7057  
Iteration 4:   log likelihood = -5038.7057  

Fitting full model:

rho =  0.0     log likelihood = -5038.7057
rho =  0.1     log likelihood = -4253.2842
rho =  0.2     log likelihood = -3925.5342
rho =  0.3     log likelihood = -3773.0047
rho =  0.4     log likelihood = -3715.2696
rho =  0.5     log likelihood = -3721.2697

Iteration 0:   log likelihood = -3715.0755  
Iteration 1:   log likelihood = -2835.1023  (not concave)
Iteration 2:   log likelihood = -2831.6157  (not concave)
Iteration 3:   log likelihood = -2831.0211  (not concave)
Iteration 4:   log likelihood = -2831.0211  (not concave)
Iteration 5:   log likelihood = -2828.4383  
Iteration 6:   log likelihood = -2826.9281  
Iteration 7:   log likelihood = -2826.4622  (not concave)
Iteration 8:   log likelihood =  -2825.847  (not concave)
Iteration 9:   log likelihood = -2825.3099  (not concave)
Iteration 10:  log likelihood = -2824.5385  (not concave)
Iteration 11:  log likelihood =  -2823.649  
Iteration 12:  log likelihood = -2821.5655  
Iteration 13:  log likelihood = -2821.1406  
Iteration 14:  log likelihood = -2821.1396  
Iteration 15:  log likelihood = -2821.1396  

Random-effects probit regression                Number of obs      =     30618
Group variable: firmnum                         Number of groups   =      5169

Random effects u_i ~ Gaussian                   Obs per group: min =         1
                                                               avg =       5.9
                                                               max =        11

Integration method: mvaghermite                 Integration points =        12

                                                Wald chi2(16)      =    378.70
Log likelihood  = -2821.1396                    Prob > chi2        =    0.0000

------------------------------------------------------------------------------
    RJVhor_m |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          MS |
         L3. |    .383614   .6789588     0.57   0.572    -.9471209    1.714349
         L3. |          0  (omitted)
             |
 patents_t_3 |   .0002886   .0002651     1.09   0.276     -.000231    .0008081
             |
         lta |
         L3. |   .3220065    .043173     7.46   0.000     .2373891    .4066239
             |
        lrd2 |
         L3. |   .2799827   .0532253     5.26   0.000     .1756631    .3843024
             |
       m_lrd |
         L1. |   .5255716   .1009202     5.21   0.000     .3277716    .7233715
             |
    m_lmkval |
         L2. |  -.1049651   .0637376    -1.65   0.100    -.2298884    .0199583
             |
         dy1 |          0  (omitted)
         dy2 |          0  (omitted)
         dy3 |          0  (omitted)
         dy4 |  -.3963261   .1450534    -2.73   0.006    -.6806256   -.1120266
         dy5 |   -.077602   .1321998    -0.59   0.557    -.3367088    .1815047
         dy6 |  -.0705036   .1292157    -0.55   0.585    -.3237616    .1827545
         dy7 |    .068857    .130248     0.53   0.597    -.1864245    .3241385
         dy8 |   .1667726   .1223385     1.36   0.173    -.0730065    .4065517
         dy9 |   .1345775   .1189728     1.13   0.258    -.0986048    .3677599
        dy10 |   .1415126   .1166987     1.21   0.225    -.0872127    .3702378
        dy11 |   .1263412   .1127694     1.12   0.263    -.0946827    .3473652
        dy12 |    .076829   .1099992     0.70   0.485    -.1387656    .2924235
        dy13 |    .041057   .1061158     0.39   0.699    -.1669261    .2490401
        dy14 |          0  (omitted)
       _cons |  -7.546239   .3291178   -22.93   0.000    -8.191298    -6.90118
-------------+----------------------------------------------------------------
    /lnsig2u |   2.100768   .0670831                      1.969287    2.232248
-------------+----------------------------------------------------------------
     sigma_u |   2.858748   .0958868                      2.676858    3.052998
         rho |   .8909778   .0065162                      .8775345    .9031083
------------------------------------------------------------------------------
Likelihood-ratio test of rho=0: chibar2(01) =  4435.13 Prob >= chibar2 = 0.000

. eststo RJV5p

. xtprobit L(0/0).RJVhor_l L(3/3).MS  L(3/3).MS patents_t_3 l(3/3).lta L(3/3).lrd2 L(1/1).m_lrd L(2/2).m_lmkval dy* if e(sample)
note: L3.MS omitted because of collinearity
note: dy1 omitted because of collinearity
note: dy2 omitted because of collinearity
note: dy3 omitted because of collinearity
note: dy14 omitted because of collinearity

Fitting comparison model:

Iteration 0:   log likelihood = -3448.7257  
Iteration 1:   log likelihood =  -2561.796  
Iteration 2:   log likelihood = -2460.8159  
Iteration 3:   log likelihood = -2459.0618  
Iteration 4:   log likelihood = -2459.0614  
Iteration 5:   log likelihood = -2459.0614  

Fitting full model:

rho =  0.0     log likelihood = -2459.0614
rho =  0.1     log likelihood = -2013.4169
rho =  0.2     log likelihood = -1836.3085
rho =  0.3     log likelihood = -1768.3491
rho =  0.4     log likelihood =  -1765.791
rho =  0.5     log likelihood = -1813.4773

Iteration 0:   log likelihood = -1765.6913  
Iteration 1:   log likelihood = -1109.5424  (not concave)
Iteration 2:   log likelihood =  -1099.939  (not concave)
Iteration 3:   log likelihood = -1096.9101  (not concave)
Iteration 4:   log likelihood = -1096.4327  (not concave)
Iteration 5:   log likelihood = -1096.1232  (not concave)
Iteration 6:   log likelihood = -1096.1232  (not concave)
Iteration 7:   log likelihood = -1087.3861  
Iteration 8:   log likelihood = -1079.7553  
Iteration 9:   log likelihood = -1079.4627  
Iteration 10:  log likelihood = -1079.3636  
Iteration 11:  log likelihood = -1079.3434  
Iteration 12:  log likelihood = -1079.3433  

Random-effects probit regression                Number of obs      =     30618
Group variable: firmnum                         Number of groups   =      5169

Random effects u_i ~ Gaussian                   Obs per group: min =         1
                                                               avg =       5.9
                                                               max =        11

Integration method: mvaghermite                 Integration points =        12

                                                Wald chi2(16)      =    328.85
Log likelihood  = -1079.3433                    Prob > chi2        =    0.0000

------------------------------------------------------------------------------
    RJVhor_l |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          MS |
         L3. |   -.887407   .8811911    -1.01   0.314     -2.61451    .8396958
         L3. |          0  (omitted)
             |
 patents_t_3 |   .0002281    .000361     0.63   0.527    -.0004794    .0009355
             |
         lta |
         L3. |   .4523775   .0743856     6.08   0.000     .3065845    .5981706
             |
        lrd2 |
         L3. |   .4294921   .0982681     4.37   0.000     .2368901     .622094
             |
       m_lrd |
         L1. |   1.479086    .166085     8.91   0.000     1.153566    1.804607
             |
    m_lmkval |
         L2. |   .3497771   .1137883     3.07   0.002     .1267561     .572798
             |
         dy1 |          0  (omitted)
         dy2 |          0  (omitted)
         dy3 |          0  (omitted)
         dy4 |  -.8553009   .2609125    -3.28   0.001     -1.36668   -.3439217
         dy5 |  -1.295221    .263547    -4.91   0.000    -1.811763   -.7786783
         dy6 |   -.665074   .2379198    -2.80   0.005    -1.131388   -.1987598
         dy7 |  -.3512658   .2381381    -1.48   0.140    -.8180079    .1154763
         dy8 |  -.5996091    .225889    -2.65   0.008    -1.042343   -.1568748
         dy9 |  -.4304293   .2178011    -1.98   0.048    -.8573117    -.003547
        dy10 |  -.5387216   .2146687    -2.51   0.012    -.9594646   -.1179786
        dy11 |  -.3304574   .2050913    -1.61   0.107    -.7324289     .071514
        dy12 |  -.3346934   .1940519    -1.72   0.085    -.7150282    .0456414
        dy13 |  -.7511235   .1876727    -4.00   0.000    -1.118955   -.3832917
        dy14 |          0  (omitted)
       _cons |  -15.75657   .6789766   -23.21   0.000    -17.08734    -14.4258
-------------+----------------------------------------------------------------
    /lnsig2u |   3.024541   .0593115                      2.908292    3.140789
-------------+----------------------------------------------------------------
     sigma_u |    4.53702   .1345488                      4.280826    4.808545
         rho |   .9536706   .0026206                      .9482548    .9585442
------------------------------------------------------------------------------
Likelihood-ratio test of rho=0: chibar2(01) =  2759.44 Prob >= chibar2 = 0.000

. eststo RJV6p

. 
. 
. ************************
. *********  RJV ANY - Table 4
. ************************
. 
. 
. xtdpd L(0/1).MS L(0/2).RJV2 L(0/2).lrd2 L(1/1).m_lmkval L(1/1).m_lrd dy2-dy13 if zero_same==0 & drop==0 &  network>=0, dgmmiv(
> MS, lag(2 5)) dgmmiv(RJV, lag(3 3)) dgmmiv(lrd2, lag(2 6))  lgmmiv(l(5/5).MS l(6/6).lrd2)  div(l(1/1).patents_t_2 L(1/2).ta) d
> iv(L(1/2).m_lmkval L(1/2).m_lrd dy2-dy13) twostep
note: dy2 dropped from div() because of collinearity
note: dy2 dropped because of collinearity
note: D.dy3 dropped because of collinearity

Dynamic panel-data estimation                Number of obs         =     36485
Group variable: firmnum                      Number of groups      =      5785
Time variable: year
                                             Obs per group:    min =         1
                                                               avg =  6.306828
                                                               max =        12

Number of instruments =    133               Wald chi2(19)         =   4999.00
                                             Prob > chi2           =    0.0000
Two-step results
------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          MS |
         L1. |   .9004759   .0146077    61.64   0.000     .8718453    .9291064
             |
        RJV2 |
         --. |   .0199117   .0102792     1.94   0.053    -.0002352    .0400586
         L1. |  -.0162311   .0106315    -1.53   0.127    -.0370685    .0046063
         L2. |  -.0060569   .0083527    -0.73   0.468    -.0224278    .0103141
             |
        lrd2 |
         --. |   .0040635    .001529     2.66   0.008     .0010667    .0070604
         L1. |   -.002751   .0014694    -1.87   0.061    -.0056309    .0001289
         L2. |  -.0007963   .0003357    -2.37   0.018    -.0014542   -.0001384
             |
    m_lmkval |
         L1. |  -.0002435    .000382    -0.64   0.524    -.0009923    .0005052
             |
       m_lrd |
         L1. |   .0031887   .0018256     1.75   0.081    -.0003894    .0067667
             |
         dy4 |  -.0000399   .0002847    -0.14   0.889    -.0005978    .0005181
         dy5 |  -.0003679   .0002863    -1.29   0.199    -.0009291    .0001932
         dy6 |  -.0003463   .0003093    -1.12   0.263    -.0009526    .0002599
         dy7 |   -.000228   .0002911    -0.78   0.433    -.0007986    .0003426
         dy8 |  -.0005561   .0002996    -1.86   0.063    -.0011432     .000031
         dy9 |  -.0003924   .0003152    -1.24   0.213    -.0010101    .0002254
        dy10 |  -.0006663   .0002975    -2.24   0.025    -.0012494   -.0000831
        dy11 |  -.0003996   .0002994    -1.33   0.182    -.0009864    .0001873
        dy12 |  -.0003021    .000283    -1.07   0.286    -.0008567    .0002526
        dy13 |  -.0003855    .000299    -1.29   0.197    -.0009715    .0002004
       _cons |  -.0002744    .001658    -0.17   0.869    -.0035241    .0029753
------------------------------------------------------------------------------
Warning: gmm two-step standard errors are biased; robust standard 
         errors are recommended.
Instruments for differenced equation
        GMM-type: L(2/5).MS L(3/3).RJV L(2/6).lrd2
        Standard: LD.patents_t_2 LD.ta L2D.ta LD.m_lmkval L2D.m_lmkval
                  LD.m_lrd L2D.m_lrd D.dy3 D.dy4 D.dy5 D.dy6 D.dy7 D.dy8
                  D.dy9 D.dy10 D.dy11 D.dy12 D.dy13
Instruments for level equation
        GMM-type: L6D.MS L7D.lrd2
        Standard: _cons

. estat sargan
Sargan test of overidentifying restrictions
        H0: overidentifying restrictions are valid

        chi2(113)    =  117.4068
        Prob > chi2  =    0.3693

. *no system
. xtdpd L(0/1).MS L(0/2).RJV2 L(0/2).lrd2 L(1/1).m_lmkval L(1/1).m_lrd dy2-dy13 if zero_same==0 & drop==0 &  network>=0, dgmmiv(
> MS, lag(2 5)) dgmmiv(RJV, lag(3 3)) dgmmiv(lrd2, lag(2 6))  div(l(1/1).patents_t_2 L(1/2).ta) div(L(1/2).m_lmkval L(1/2).m_lrd
>  dy2-dy13) twostep
note: dy2 dropped from div() because of collinearity
note: dy2 dropped because of collinearity
note: D.dy3 dropped because of collinearity

Dynamic panel-data estimation                Number of obs         =     36485
Group variable: firmnum                      Number of groups      =      5785
Time variable: year
                                             Obs per group:    min =         1
                                                               avg =  6.306828
                                                               max =        12

Number of instruments =    120               Wald chi2(19)         =   1457.90
                                             Prob > chi2           =    0.0000
Two-step results
------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          MS |
         L1. |   .8540766   .0255829    33.38   0.000     .8039351    .9042181
             |
        RJV2 |
         --. |   .0141013    .011139     1.27   0.206    -.0077308    .0359335
         L1. |  -.0162615   .0124386    -1.31   0.191    -.0406408    .0081178
         L2. |  -.0027434   .0099038    -0.28   0.782    -.0221545    .0166677
             |
        lrd2 |
         --. |   .0027941   .0027446     1.02   0.309    -.0025852    .0081733
         L1. |  -.0028602   .0016091    -1.78   0.075    -.0060139    .0002935
         L2. |  -.0005272   .0003808    -1.38   0.166    -.0012737    .0002192
             |
    m_lmkval |
         L1. |    .000068   .0004417     0.15   0.878    -.0007977    .0009337
             |
       m_lrd |
         L1. |   .0041739   .0022462     1.86   0.063    -.0002286    .0085763
             |
         dy4 |  -.0000442   .0002962    -0.15   0.881    -.0006248    .0005364
         dy5 |   -.000514   .0003149    -1.63   0.103    -.0011312    .0001032
         dy6 |  -.0003312   .0003313    -1.00   0.317    -.0009804    .0003181
         dy7 |  -.0002952   .0003188    -0.93   0.355      -.00092    .0003297
         dy8 |  -.0007113   .0003291    -2.16   0.031    -.0013564   -.0000663
         dy9 |  -.0005246    .000342    -1.53   0.125    -.0011948    .0001457
        dy10 |   -.000701   .0003109    -2.26   0.024    -.0013104   -.0000917
        dy11 |  -.0003325   .0003215    -1.03   0.301    -.0009627    .0002977
        dy12 |  -.0002742   .0002985    -0.92   0.358    -.0008593    .0003109
        dy13 |  -.0004059   .0003193    -1.27   0.204    -.0010317    .0002198
       _cons |   .0002088   .0018878     0.11   0.912    -.0034913    .0039088
------------------------------------------------------------------------------
Warning: gmm two-step standard errors are biased; robust standard 
         errors are recommended.
Instruments for differenced equation
        GMM-type: L(2/5).MS L(3/3).RJV L(2/6).lrd2
        Standard: LD.patents_t_2 LD.ta L2D.ta LD.m_lmkval L2D.m_lmkval
                  LD.m_lrd L2D.m_lrd D.dy3 D.dy4 D.dy5 D.dy6 D.dy7 D.dy8
                  D.dy9 D.dy10 D.dy11 D.dy12 D.dy13
Instruments for level equation
        Standard: _cons

. estat sargan
Sargan test of overidentifying restrictions
        H0: overidentifying restrictions are valid

        chi2(100)    =  102.2482
        Prob > chi2  =    0.4188

. xtdpd L(0/1).MS L(0/2).RJV2 L(0/2).lrd2 L(1/1).m_lmkval L(1/1).m_lrd dy2-dy13 if zero_same==0 & drop==0 &  network>=0, dgmmiv(
> MS, lag(2 5)) dgmmiv(RJV, lag(3 3)) dgmmiv(lrd2, lag(2 6))  lgmmiv(l(5/5).MS l(6/6).lrd2)  div(l(1/1).patents_t_2 L(1/2).ta) d
> iv(L(1/2).m_lmkval L(1/2).m_lrd dy2-dy13) twostep vce(r)
note: dy2 dropped from div() because of collinearity
note: dy2 dropped because of collinearity
note: D.dy3 dropped because of collinearity

Dynamic panel-data estimation                Number of obs         =     36485
Group variable: firmnum                      Number of groups      =      5785
Time variable: year
                                             Obs per group:    min =         1
                                                               avg =  6.306828
                                                               max =        12

Number of instruments =    133               Wald chi2(19)         =    461.70
                                             Prob > chi2           =    0.0000
Two-step results
                                (Std. Err. adjusted for clustering on firmnum)
------------------------------------------------------------------------------
             |              WC-Robust
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          MS |
         L1. |   .9004759   .0728152    12.37   0.000     .7577607    1.043191
             |
        RJV2 |
         --. |   .0199117   .0125055     1.59   0.111    -.0045986    .0444219
         L1. |  -.0162311   .0139796    -1.16   0.246    -.0436306    .0111684
         L2. |  -.0060569   .0114883    -0.53   0.598    -.0285735    .0164597
             |
        lrd2 |
         --. |   .0040635   .0018549     2.19   0.028      .000428    .0076991
         L1. |   -.002751   .0018369    -1.50   0.134    -.0063513    .0008492
         L2. |  -.0007963   .0003693    -2.16   0.031    -.0015201   -.0000726
             |
    m_lmkval |
         L1. |  -.0002435   .0004226    -0.58   0.564    -.0010717    .0005847
             |
       m_lrd |
         L1. |   .0031887   .0023189     1.38   0.169    -.0013563    .0077336
             |
         dy4 |  -.0000399   .0003682    -0.11   0.914    -.0007615    .0006818
         dy5 |  -.0003679   .0002993    -1.23   0.219    -.0009546    .0002188
         dy6 |  -.0003463   .0003186    -1.09   0.277    -.0009707    .0002781
         dy7 |   -.000228   .0003431    -0.66   0.506    -.0009005    .0004444
         dy8 |  -.0005561   .0003219    -1.73   0.084     -.001187    .0000749
         dy9 |  -.0003924   .0003617    -1.08   0.278    -.0011014    .0003166
        dy10 |  -.0006663   .0003623    -1.84   0.066    -.0013763    .0000438
        dy11 |  -.0003996   .0003461    -1.15   0.248    -.0010779    .0002788
        dy12 |  -.0003021   .0003066    -0.99   0.325    -.0009031    .0002989
        dy13 |  -.0003855   .0003493    -1.10   0.270    -.0010701    .0002991
       _cons |  -.0002744   .0022096    -0.12   0.901    -.0046052    .0040564
------------------------------------------------------------------------------
Instruments for differenced equation
        GMM-type: L(2/5).MS L(3/3).RJV L(2/6).lrd2
        Standard: LD.patents_t_2 LD.ta L2D.ta LD.m_lmkval L2D.m_lmkval
                  LD.m_lrd L2D.m_lrd D.dy3 D.dy4 D.dy5 D.dy6 D.dy7 D.dy8
                  D.dy9 D.dy10 D.dy11 D.dy12 D.dy13
Instruments for level equation
        GMM-type: L6D.MS L7D.lrd2
        Standard: _cons

. estat abond

Arellano-Bond test for zero autocorrelation in first-differenced errors
  +-----------------------+
  |Order |  z     Prob > z|
  |------+----------------|
  |   1  |-4.8608  0.0000 |
  |   2  | 1.2073  0.2273 |
  +-----------------------+
   H0: no autocorrelation 

. eststo any

. lincom RJV2+L1.RJV2+l2.RJV2

 ( 1)  RJV2 + L.RJV2 + L2.RJV2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0023763   .0050096    -0.47   0.635     -.012195    .0074424
------------------------------------------------------------------------------

. lincom lrd2+L1.lrd2+l2.lrd2

 ( 1)  lrd2 + L.lrd2 + L2.lrd2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0005162   .0012896     0.40   0.689    -.0020113    .0030437
------------------------------------------------------------------------------

. 
. displ chi2(13,12.2)
.48868395

. 
. 
. ************************
. *********  HORIZONTAL vs. VERTICAL RJV - Table 4
. ************************
. 
. xtdpd L(0/1).MS L(0/2).RJVhor L(0/2).RJVver L(0/2).lrd2 L(1/1).m_lmkval L(1/1).m_lrd dy2-dy13 if zero_same==0 & drop==0 , dgmm
> iv(MS, lag(2 5)) dgmmiv(RJVhor, lag(3 3)) dgmmiv(RJVver, lag(3 3)) dgmmiv(lrd2, lag(3 5))  lgmmiv(l(6/6).MS l(6/6).lrd2)  div(
> l(1/1).patents_t_2 L(2/2).ta) div(L(1/2).m_lmkval L(1/2).m_lrd dy2-dy13) twostep
note: dy2 dropped from div() because of collinearity
note: dy2 dropped because of collinearity
note: D.dy3 dropped because of collinearity

Dynamic panel-data estimation                Number of obs         =     36485
Group variable: firmnum                      Number of groups      =      5785
Time variable: year
                                             Obs per group:    min =         1
                                                               avg =  6.306828
                                                               max =        12

Number of instruments =    123               Wald chi2(22)         =   4290.81
                                             Prob > chi2           =    0.0000
Two-step results
------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          MS |
         L1. |   .9229829   .0176451    52.31   0.000     .8883993    .9575666
             |
      RJVhor |
         --. |    .021638   .0152069     1.42   0.155     -.008167    .0514431
         L1. |  -.0401552   .0159485    -2.52   0.012    -.0714136   -.0088968
         L2. |   .0093781   .0122415     0.77   0.444    -.0146147    .0333709
             |
      RJVver |
         --. |   .0571624   .0177252     3.22   0.001     .0224216    .0919032
         L1. |   .0221044   .0181171     1.22   0.222    -.0134044    .0576133
         L2. |  -.0311508   .0133182    -2.34   0.019    -.0572539   -.0050477
             |
        lrd2 |
         --. |   .0069641   .0031398     2.22   0.027     .0008102    .0131179
         L1. |  -.0054794   .0036611    -1.50   0.134    -.0126549    .0016962
         L2. |  -.0027267     .00193    -1.41   0.158    -.0065094    .0010559
             |
    m_lmkval |
         L1. |  -.0004379   .0004772    -0.92   0.359    -.0013731    .0004973
             |
       m_lrd |
         L1. |   .0063001   .0027315     2.31   0.021     .0009465    .0116537
             |
         dy4 |   7.89e-06   .0003551     0.02   0.982    -.0006881    .0007039
         dy5 |   -.000426   .0003675    -1.16   0.246    -.0011464    .0002943
         dy6 |  -.0003285   .0003997    -0.82   0.411     -.001112    .0004549
         dy7 |  -.0001901   .0003775    -0.50   0.615    -.0009299    .0005497
         dy8 |  -.0005988   .0004001    -1.50   0.135    -.0013829    .0001854
         dy9 |  -.0003125   .0004191    -0.75   0.456    -.0011338    .0005089
        dy10 |  -.0006192   .0003799    -1.63   0.103    -.0013638    .0001254
        dy11 |  -.0002577   .0003819    -0.67   0.500    -.0010062    .0004908
        dy12 |  -.0002723   .0003601    -0.76   0.450     -.000978    .0004334
        dy13 |  -.0003173   .0003735    -0.85   0.396    -.0010493    .0004148
       _cons |  -.0015047   .0023746    -0.63   0.526    -.0061589    .0031494
------------------------------------------------------------------------------
Warning: gmm two-step standard errors are biased; robust standard 
         errors are recommended.
Instruments for differenced equation
        GMM-type: L(2/5).MS L(3/3).RJVhor L(3/3).RJVver L(3/5).lrd2
        Standard: LD.patents_t_2 L2D.ta LD.m_lmkval L2D.m_lmkval LD.m_lrd
                  L2D.m_lrd D.dy3 D.dy4 D.dy5 D.dy6 D.dy7 D.dy8 D.dy9 D.dy10
                  D.dy11 D.dy12 D.dy13
Instruments for level equation
        GMM-type: L7D.MS L7D.lrd2
        Standard: _cons

. estat sargan
Sargan test of overidentifying restrictions
        H0: overidentifying restrictions are valid

        chi2(100)    =  87.65224
        Prob > chi2  =    0.8063

. *no system
. xtdpd L(0/1).MS L(0/2).RJVhor L(0/2).RJVver L(0/2).lrd2 L(1/1).m_lmkval L(1/1).m_lrd dy2-dy13 if zero_same==0 & drop==0 , dgmm
> iv(MS, lag(2 5)) dgmmiv(RJVhor, lag(3 3)) dgmmiv(RJVver, lag(3 3)) dgmmiv(lrd2, lag(3 5)) div(l(1/1).patents_t_2 L(2/2).ta) di
> v(L(1/2).m_lmkval L(1/2).m_lrd dy2-dy13) twostep
note: dy2 dropped from div() because of collinearity
note: dy2 dropped because of collinearity
note: D.dy3 dropped because of collinearity

Dynamic panel-data estimation                Number of obs         =     36485
Group variable: firmnum                      Number of groups      =      5785
Time variable: year
                                             Obs per group:    min =         1
                                                               avg =  6.306828
                                                               max =        12

Number of instruments =    111               Wald chi2(22)         =   1276.28
                                             Prob > chi2           =    0.0000
Two-step results
------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          MS |
         L1. |   .8623271   .0293146    29.42   0.000     .8048715    .9197826
             |
      RJVhor |
         --. |   .0194407   .0156738     1.24   0.215    -.0112794    .0501608
         L1. |  -.0431478   .0164127    -2.63   0.009    -.0753161   -.0109795
         L2. |   .0116565    .012175     0.96   0.338    -.0122062    .0355191
             |
      RJVver |
         --. |   .0459906   .0184809     2.49   0.013     .0097686    .0822126
         L1. |   .0193627   .0189015     1.02   0.306    -.0176835    .0564089
         L2. |  -.0237682   .0137865    -1.72   0.085    -.0507893    .0032528
             |
        lrd2 |
         --. |   .0072943   .0052207     1.40   0.162     -.002938    .0175267
         L1. |  -.0063448   .0038871    -1.63   0.103    -.0139634    .0012738
         L2. |  -.0020428   .0020275    -1.01   0.314    -.0060167    .0019311
             |
    m_lmkval |
         L1. |  -.0002822   .0005331    -0.53   0.597     -.001327    .0007626
             |
       m_lrd |
         L1. |   .0071737   .0029991     2.39   0.017     .0012955    .0130518
             |
         dy4 |   .0000717   .0003513     0.20   0.838    -.0006169    .0007603
         dy5 |  -.0003339   .0003796    -0.88   0.379     -.001078    .0004102
         dy6 |   -.000257   .0003977    -0.65   0.518    -.0010365    .0005226
         dy7 |  -.0002429   .0003766    -0.64   0.519    -.0009809    .0004952
         dy8 |  -.0006224   .0003994    -1.56   0.119    -.0014051    .0001603
         dy9 |   -.000322    .000418    -0.77   0.441    -.0011413    .0004973
        dy10 |  -.0005922   .0003778    -1.57   0.117    -.0013328    .0001483
        dy11 |  -.0002663   .0004061    -0.66   0.512    -.0010624    .0005297
        dy12 |  -.0002304   .0003795    -0.61   0.544    -.0009743    .0005134
        dy13 |  -.0003366   .0003818    -0.88   0.378    -.0010849    .0004118
       _cons |  -.0013425   .0027363    -0.49   0.624    -.0067057    .0040206
------------------------------------------------------------------------------
Warning: gmm two-step standard errors are biased; robust standard 
         errors are recommended.
Instruments for differenced equation
        GMM-type: L(2/5).MS L(3/3).RJVhor L(3/3).RJVver L(3/5).lrd2
        Standard: LD.patents_t_2 L2D.ta LD.m_lmkval L2D.m_lmkval LD.m_lrd
                  L2D.m_lrd D.dy3 D.dy4 D.dy5 D.dy6 D.dy7 D.dy8 D.dy9 D.dy10
                  D.dy11 D.dy12 D.dy13
Instruments for level equation
        Standard: _cons

. estat sargan
Sargan test of overidentifying restrictions
        H0: overidentifying restrictions are valid

        chi2(88)     =  84.43591
        Prob > chi2  =    0.5878

. xtdpd L(0/1).MS L(0/2).RJVhor L(0/2).RJVver L(0/2).lrd2 L(1/1).m_lmkval L(1/1).m_lrd dy2-dy13 if zero_same==0 & drop==0 , dgmm
> iv(MS, lag(2 5)) dgmmiv(RJVhor, lag(3 3)) dgmmiv(RJVver, lag(3 3)) dgmmiv(lrd2, lag(3 5))  lgmmiv(l(6/6).MS l(6/6).lrd2)  div(
> l(1/1).patents_t_2 L(2/2).ta) div(L(1/2).m_lmkval L(1/2).m_lrd dy2-dy13) twostep vce(r)
note: dy2 dropped from div() because of collinearity
note: dy2 dropped because of collinearity
note: D.dy3 dropped because of collinearity

Dynamic panel-data estimation                Number of obs         =     36485
Group variable: firmnum                      Number of groups      =      5785
Time variable: year
                                             Obs per group:    min =         1
                                                               avg =  6.306828
                                                               max =        12

Number of instruments =    123               Wald chi2(22)         =    904.38
                                             Prob > chi2           =    0.0000
Two-step results
                                (Std. Err. adjusted for clustering on firmnum)
------------------------------------------------------------------------------
             |              WC-Robust
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          MS |
         L1. |   .9229829    .043725    21.11   0.000     .8372836    1.008682
             |
      RJVhor |
         --. |    .021638   .0164994     1.31   0.190    -.0107002    .0539763
         L1. |  -.0401552   .0261173    -1.54   0.124    -.0913442    .0110338
         L2. |   .0093781   .0192354     0.49   0.626    -.0283225    .0470788
             |
      RJVver |
         --. |   .0571624   .0250363     2.28   0.022     .0080921    .1062327
         L1. |   .0221044   .0206152     1.07   0.284    -.0183005    .0625094
         L2. |  -.0311508   .0186905    -1.67   0.096    -.0677836     .005482
             |
        lrd2 |
         --. |   .0069641   .0038565     1.81   0.071    -.0005945    .0145226
         L1. |  -.0054794   .0046238    -1.19   0.236    -.0145418     .003583
         L2. |  -.0027267   .0023306    -1.17   0.242    -.0072946    .0018412
             |
    m_lmkval |
         L1. |  -.0004379   .0005301    -0.83   0.409    -.0014768     .000601
             |
       m_lrd |
         L1. |   .0063001   .0036652     1.72   0.086    -.0008835    .0134837
             |
         dy4 |   7.89e-06    .000445     0.02   0.986    -.0008644    .0008801
         dy5 |   -.000426   .0003837    -1.11   0.267    -.0011781    .0003261
         dy6 |  -.0003285   .0004035    -0.81   0.416    -.0011194    .0004623
         dy7 |  -.0001901   .0003912    -0.49   0.627    -.0009569    .0005767
         dy8 |  -.0005988   .0004132    -1.45   0.147    -.0014086    .0002111
         dy9 |  -.0003125   .0004754    -0.66   0.511    -.0012442    .0006193
        dy10 |  -.0006192   .0004429    -1.40   0.162    -.0014873    .0002489
        dy11 |  -.0002577    .000418    -0.62   0.538     -.001077    .0005616
        dy12 |  -.0002723   .0004125    -0.66   0.509    -.0010808    .0005363
        dy13 |  -.0003173     .00045    -0.70   0.481    -.0011993    .0005648
       _cons |  -.0015047   .0027623    -0.54   0.586    -.0069187    .0039092
------------------------------------------------------------------------------
Instruments for differenced equation
        GMM-type: L(2/5).MS L(3/3).RJVhor L(3/3).RJVver L(3/5).lrd2
        Standard: LD.patents_t_2 L2D.ta LD.m_lmkval L2D.m_lmkval LD.m_lrd
                  L2D.m_lrd D.dy3 D.dy4 D.dy5 D.dy6 D.dy7 D.dy8 D.dy9 D.dy10
                  D.dy11 D.dy12 D.dy13
Instruments for level equation
        GMM-type: L7D.MS L7D.lrd2
        Standard: _cons

. estat abond

Arellano-Bond test for zero autocorrelation in first-differenced errors
  +-----------------------+
  |Order |  z     Prob > z|
  |------+----------------|
  |   1  |-5.2926  0.0000 |
  |   2  | .40443  0.6859 |
  +-----------------------+
   H0: no autocorrelation 

. eststo hor_ver

. lincom RJVhor+L1.RJVhor+l2.RJVhor

 ( 1)  RJVhor + L.RJVhor + L2.RJVhor = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0091391   .0073413    -1.24   0.213    -.0235277    .0052495
------------------------------------------------------------------------------

. lincom RJVver+L1.RJVver+l2.RJVver

 ( 1)  RJVver + L.RJVver + L2.RJVver = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |    .048116   .0205421     2.34   0.019     .0078542    .0883778
------------------------------------------------------------------------------

. lincom lrd2+L1.lrd2+l2.lrd2

 ( 1)  lrd2 + L.lrd2 + L2.lrd2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0012421   .0014236    -0.87   0.383    -.0040323    .0015482
------------------------------------------------------------------------------

. 
. displ chi2(12,10.2)
.40158029

. 
. ************************
. *********  HORIZONTAL SMALL - MEDIUM - LARGE vs VERTICAL RJV - Table 4
. ************************
. 
. 
. xtdpd L(0/1).MS L(0/2).RJVhor_s L(0/2).RJVhor_m L(0/2).RJVhor_l L(0/2).RJVver L(0/2).lrd2 L(1/1).m_lmkval L(1/1).m_lrd dy2-dy1
> 3 if zero_same==0 & drop==0, dgmmiv(MS, lag(2 5)) dgmmiv(RJVhor_s, lag(3 3))  dgmmiv(RJVhor_m, lag(3 3))  dgmmiv(RJVhor_l, lag
> (3 3)) dgmmiv(RJVver, lag(3 3)) dgmmiv(lrd2, lag(2 6))  lgmmiv(l(6/6).MS l(6/6).lrd2)  div(l(1/1).patents_t_2 L(2/2).ta) div(L
> (1/2).m_lmkval L(1/2).m_lrd dy2-dy13) twostep
note: dy2 dropped from div() because of collinearity
note: dy2 dropped because of collinearity
note: D.dy3 dropped because of collinearity

Dynamic panel-data estimation                Number of obs         =     36485
Group variable: firmnum                      Number of groups      =      5785
Time variable: year
                                             Obs per group:    min =         1
                                                               avg =  6.306828
                                                               max =        12

Number of instruments =    164               Wald chi2(28)         =   4738.59
                                             Prob > chi2           =    0.0000
Two-step results
------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          MS |
         L1. |   .9159475   .0174662    52.44   0.000     .8817144    .9501806
             |
    RJVhor_s |
         --. |   .0123231   .0143458     0.86   0.390    -.0157941    .0404402
         L1. |  -.0267183   .0130444    -2.05   0.041    -.0522848   -.0011517
         L2. |   .0048576   .0099724     0.49   0.626    -.0146879    .0244031
             |
    RJVhor_m |
         --. |    .018411   .0137146     1.34   0.179    -.0084691    .0452911
         L1. |  -.0281164   .0143189    -1.96   0.050    -.0561808   -.0000519
         L2. |   -.003971   .0098213    -0.40   0.686    -.0232204    .0152783
             |
    RJVhor_l |
         --. |   .0127501   .0145088     0.88   0.380    -.0156867    .0411868
         L1. |  -.0191948   .0133845    -1.43   0.152     -.045428    .0070384
         L2. |  -.0200496   .0119312    -1.68   0.093    -.0434344    .0033352
             |
      RJVver |
         --. |   .0495163   .0149164     3.32   0.001     .0202806     .078752
         L1. |   .0302401   .0142572     2.12   0.034     .0022966    .0581836
         L2. |  -.0352001   .0114976    -3.06   0.002    -.0577351   -.0126651
             |
        lrd2 |
         --. |   .0023734   .0017387     1.37   0.172    -.0010344    .0057811
         L1. |  -.0008784   .0016825    -0.52   0.602     -.004176    .0024192
         L2. |   -.000715   .0003781    -1.89   0.059    -.0014561     .000026
             |
    m_lmkval |
         L1. |  -.0001286   .0004446    -0.29   0.772        -.001    .0007427
             |
       m_lrd |
         L1. |   .0023782   .0019558     1.22   0.224     -.001455    .0062114
             |
         dy4 |  -.0001793   .0003162    -0.57   0.571    -.0007991    .0004405
         dy5 |   -.000357   .0003325    -1.07   0.283    -.0010087    .0002946
         dy6 |  -.0001364   .0003738    -0.36   0.715     -.000869    .0005963
         dy7 |  -.0001609   .0003361    -0.48   0.632    -.0008196    .0004978
         dy8 |  -.0004341   .0003589    -1.21   0.226    -.0011376    .0002693
         dy9 |  -.0001047   .0003697    -0.28   0.777    -.0008293    .0006199
        dy10 |  -.0003736   .0003427    -1.09   0.276    -.0010452     .000298
        dy11 |  -.0000369    .000345    -0.11   0.915    -.0007131    .0006394
        dy12 |  -.0001643   .0003165    -0.52   0.604    -.0007846     .000456
        dy13 |  -.0003303   .0003417    -0.97   0.334    -.0010001    .0003395
       _cons |  -.0005155   .0020521    -0.25   0.802    -.0045376    .0035067
------------------------------------------------------------------------------
Warning: gmm two-step standard errors are biased; robust standard 
         errors are recommended.
Instruments for differenced equation
        GMM-type: L(2/5).MS L(3/3).RJVhor_s L(3/3).RJVhor_m L(3/3).RJVhor_l
                  L(3/3).RJVver L(2/6).lrd2
        Standard: LD.patents_t_2 L2D.ta LD.m_lmkval L2D.m_lmkval LD.m_lrd
                  L2D.m_lrd D.dy3 D.dy4 D.dy5 D.dy6 D.dy7 D.dy8 D.dy9 D.dy10
                  D.dy11 D.dy12 D.dy13
Instruments for level equation
        GMM-type: L7D.MS L7D.lrd2
        Standard: _cons

. estat sargan
Sargan test of overidentifying restrictions
        H0: overidentifying restrictions are valid

        chi2(135)    =   108.911
        Prob > chi2  =    0.9518

. *no system
. xtdpd L(0/1).MS L(0/2).RJVhor_s L(0/2).RJVhor_m L(0/2).RJVhor_l L(0/2).RJVver L(0/2).lrd2 L(1/1).m_lmkval L(1/1).m_lrd dy2-dy1
> 3 if zero_same==0 & drop==0, dgmmiv(MS, lag(2 5)) dgmmiv(RJVhor_s, lag(3 3))  dgmmiv(RJVhor_m, lag(3 3))  dgmmiv(RJVhor_l, lag
> (3 3)) dgmmiv(RJVver, lag(3 3)) dgmmiv(lrd2, lag(2 6)) div(l(1/1).patents_t_2 L(2/2).ta) div(L(1/2).m_lmkval L(1/2).m_lrd dy2-
> dy13) twostep
note: dy2 dropped from div() because of collinearity
note: dy2 dropped because of collinearity
note: D.dy3 dropped because of collinearity

Dynamic panel-data estimation                Number of obs         =     36485
Group variable: firmnum                      Number of groups      =      5785
Time variable: year
                                             Obs per group:    min =         1
                                                               avg =  6.306828
                                                               max =        12

Number of instruments =    152               Wald chi2(28)         =   1668.09
                                             Prob > chi2           =    0.0000
Two-step results
------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          MS |
         L1. |   .8190104   .0247634    33.07   0.000      .770475    .8675458
             |
    RJVhor_s |
         --. |   .0108076   .0136933     0.79   0.430    -.0160307    .0376459
         L1. |  -.0219153   .0122391    -1.79   0.073    -.0459035    .0020729
         L2. |    .000447   .0091948     0.05   0.961    -.0175744    .0184685
             |
    RJVhor_m |
         --. |   .0146516   .0132479     1.11   0.269    -.0113139     .040617
         L1. |  -.0246209   .0134234    -1.83   0.067    -.0509302    .0016885
         L2. |  -.0078622   .0091894    -0.86   0.392    -.0258731    .0101487
             |
    RJVhor_l |
         --. |   .0019203   .0140761     0.14   0.891    -.0256684     .029509
         L1. |  -.0253207   .0124667    -2.03   0.042    -.0497549   -.0008864
         L2. |  -.0192457    .011705    -1.64   0.100    -.0421871    .0036957
             |
      RJVver |
         --. |   .0388874   .0144856     2.68   0.007     .0104962    .0672786
         L1. |   .0285053   .0137612     2.07   0.038     .0015338    .0554768
         L2. |  -.0319885   .0110097    -2.91   0.004    -.0535671   -.0104098
             |
        lrd2 |
         --. |   .0048753   .0027871     1.75   0.080    -.0005874    .0103379
         L1. |  -.0007942   .0016203    -0.49   0.624    -.0039699    .0023816
         L2. |  -.0005276   .0003668    -1.44   0.150    -.0012465    .0001912
             |
    m_lmkval |
         L1. |   .0000388   .0004653     0.08   0.934    -.0008733    .0009509
             |
       m_lrd |
         L1. |   .0022104   .0020719     1.07   0.286    -.0018503    .0062712
             |
         dy4 |  -.0001123   .0003035    -0.37   0.711    -.0007072    .0004825
         dy5 |  -.0002256   .0003242    -0.70   0.486    -.0008609    .0004097
         dy6 |   .0000237   .0003674     0.06   0.949    -.0006964    .0007438
         dy7 |  -.0000631   .0003355    -0.19   0.851    -.0007208    .0005945
         dy8 |   -.000402   .0003536    -1.14   0.256    -.0010951    .0002911
         dy9 |  -.0001381   .0003614    -0.38   0.702    -.0008464    .0005701
        dy10 |  -.0003891   .0003265    -1.19   0.233     -.001029    .0002508
        dy11 |  -.0001275   .0003382    -0.38   0.706    -.0007903    .0005353
        dy12 |  -.0002111   .0003043    -0.69   0.488    -.0008075    .0003853
        dy13 |  -.0004766   .0003281    -1.45   0.146    -.0011195    .0001664
       _cons |  -.0007205   .0022042    -0.33   0.744    -.0050407    .0035996
------------------------------------------------------------------------------
Warning: gmm two-step standard errors are biased; robust standard 
         errors are recommended.
Instruments for differenced equation
        GMM-type: L(2/5).MS L(3/3).RJVhor_s L(3/3).RJVhor_m L(3/3).RJVhor_l
                  L(3/3).RJVver L(2/6).lrd2
        Standard: LD.patents_t_2 L2D.ta LD.m_lmkval L2D.m_lmkval LD.m_lrd
                  L2D.m_lrd D.dy3 D.dy4 D.dy5 D.dy6 D.dy7 D.dy8 D.dy9 D.dy10
                  D.dy11 D.dy12 D.dy13
Instruments for level equation
        Standard: _cons

. estat sargan
Sargan test of overidentifying restrictions
        H0: overidentifying restrictions are valid

        chi2(123)    =   105.954
        Prob > chi2  =    0.8640

. xtdpd L(0/1).MS L(0/2).RJVhor_s L(0/2).RJVhor_m L(0/2).RJVhor_l L(0/2).RJVver L(0/2).lrd2 L(1/1).m_lmkval L(1/1).m_lrd dy2-dy1
> 3 if zero_same==0 & drop==0, dgmmiv(MS, lag(2 5)) dgmmiv(RJVhor_s, lag(3 3))  dgmmiv(RJVhor_m, lag(3 3))  dgmmiv(RJVhor_l, lag
> (3 3)) dgmmiv(RJVver, lag(3 3)) dgmmiv(lrd2, lag(2 6))  lgmmiv(l(6/6).MS l(6/6).lrd2)  div(l(1/1).patents_t_2 L(2/2).ta) div(L
> (1/2).m_lmkval L(1/2).m_lrd dy2-dy13) twostep vce(r)
note: dy2 dropped from div() because of collinearity
note: dy2 dropped because of collinearity
note: D.dy3 dropped because of collinearity

Dynamic panel-data estimation                Number of obs         =     36485
Group variable: firmnum                      Number of groups      =      5785
Time variable: year
                                             Obs per group:    min =         1
                                                               avg =  6.306828
                                                               max =        12

Number of instruments =    164               Wald chi2(28)         =   1058.37
                                             Prob > chi2           =    0.0000
Two-step results
                                (Std. Err. adjusted for clustering on firmnum)
------------------------------------------------------------------------------
             |              WC-Robust
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          MS |
         L1. |   .9159475   .0437702    20.93   0.000     .8301596    1.001735
             |
    RJVhor_s |
         --. |   .0123231   .0177023     0.70   0.486    -.0223728    .0470189
         L1. |  -.0267183    .023614    -1.13   0.258    -.0730008    .0195642
         L2. |   .0048576   .0177497     0.27   0.784    -.0299313    .0396465
             |
    RJVhor_m |
         --. |    .018411   .0174834     1.05   0.292    -.0158558    .0526778
         L1. |  -.0281164   .0231768    -1.21   0.225    -.0735421    .0173094
         L2. |   -.003971   .0165301    -0.24   0.810    -.0363695    .0284274
             |
    RJVhor_l |
         --. |   .0127501   .0193319     0.66   0.510    -.0251398    .0506399
         L1. |  -.0191948   .0220509    -0.87   0.384    -.0624137    .0240241
         L2. |  -.0200496    .022295    -0.90   0.368     -.063747    .0236478
             |
      RJVver |
         --. |   .0495163   .0233094     2.12   0.034     .0038307    .0952019
         L1. |   .0302401   .0187273     1.61   0.106    -.0064648    .0669449
         L2. |  -.0352001   .0177844    -1.98   0.048    -.0700569   -.0003433
             |
        lrd2 |
         --. |   .0023734   .0021753     1.09   0.275    -.0018901    .0066368
         L1. |  -.0008784   .0022391    -0.39   0.695    -.0052669    .0035101
         L2. |   -.000715   .0004179    -1.71   0.087     -.001534     .000104
             |
    m_lmkval |
         L1. |  -.0001286   .0005024    -0.26   0.798    -.0011133    .0008561
             |
       m_lrd |
         L1. |   .0023782   .0024016     0.99   0.322    -.0023289    .0070853
             |
         dy4 |  -.0001793   .0003456    -0.52   0.604    -.0008567    .0004981
         dy5 |   -.000357   .0003568    -1.00   0.317    -.0010563    .0003422
         dy6 |  -.0001364   .0004024    -0.34   0.735     -.000925    .0006523
         dy7 |  -.0001609   .0003558    -0.45   0.651    -.0008583    .0005364
         dy8 |  -.0004341   .0003974    -1.09   0.275    -.0012131    .0003448
         dy9 |  -.0001047   .0004318    -0.24   0.808     -.000951    .0007416
        dy10 |  -.0003736   .0003881    -0.96   0.336    -.0011343    .0003871
        dy11 |  -.0000369   .0003832    -0.10   0.923    -.0007879    .0007141
        dy12 |  -.0001643   .0003562    -0.46   0.645    -.0008625    .0005338
        dy13 |  -.0003303   .0003855    -0.86   0.392    -.0010859    .0004253
       _cons |  -.0005155    .002387    -0.22   0.829    -.0051938    .0041629
------------------------------------------------------------------------------
Instruments for differenced equation
        GMM-type: L(2/5).MS L(3/3).RJVhor_s L(3/3).RJVhor_m L(3/3).RJVhor_l
                  L(3/3).RJVver L(2/6).lrd2
        Standard: LD.patents_t_2 L2D.ta LD.m_lmkval L2D.m_lmkval LD.m_lrd
                  L2D.m_lrd D.dy3 D.dy4 D.dy5 D.dy6 D.dy7 D.dy8 D.dy9 D.dy10
                  D.dy11 D.dy12 D.dy13
Instruments for level equation
        GMM-type: L7D.MS L7D.lrd2
        Standard: _cons

. estat abond

Arellano-Bond test for zero autocorrelation in first-differenced errors
  +-----------------------+
  |Order |  z     Prob > z|
  |------+----------------|
  |   1  |-5.0269  0.0000 |
  |   2  | .53344  0.5937 |
  +-----------------------+
   H0: no autocorrelation 

. eststo hor_sml

. lincom RJVhor_s+L1.RJVhor_s+l2.RJVhor_s

 ( 1)  RJVhor_s + L.RJVhor_s + L2.RJVhor_s = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0095376   .0129048    -0.74   0.460    -.0348305    .0157552
------------------------------------------------------------------------------

. lincom RJVhor_m+L1.RJVhor_m+l2.RJVhor_m

 ( 1)  RJVhor_m + L.RJVhor_m + L2.RJVhor_m = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0136764   .0070643    -1.94   0.053    -.0275221    .0001693
------------------------------------------------------------------------------

. lincom RJVhor_l+L1.RJVhor_l+l2.RJVhor_l

 ( 1)  RJVhor_l + L.RJVhor_l + L2.RJVhor_l = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0264943    .013769    -1.92   0.054    -.0534811    .0004925
------------------------------------------------------------------------------

. lincom RJVver+L1.RJVver+l2.RJVver

 ( 1)  RJVver + L.RJVver + L2.RJVver = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0445563   .0204541     2.18   0.029     .0044671    .0846455
------------------------------------------------------------------------------

. lincom lrd2+L1.lrd2+l2.lrd2

 ( 1)  lrd2 + L.lrd2 + L2.lrd2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0007799   .0012284     0.63   0.525    -.0016276    .0031875
------------------------------------------------------------------------------

. 
. displ chi2(13,13.1)
.55988259

. 
. /*
> predict MSpred
> predict  MSpred_sd, stdp
> gen lowerpred =MSpred- 1.96 * MSpred_sd
> gen upperpred = MSpred+ 1.96 * MSpred_sd
> */
. 
. ***********************
. **** HORIZONTAL SMALL - MEDIUM - LARGE vs VERTICAL RJV  
. ****    network based on MS    -   Table 12
. ***********************
. 
. 
. xtdpd L(0/1).MS L(0/2).RJVhorMS_s L(0/2).RJVhorMS_m L(0/2).RJVhorMS_l L(0/2).RJVver L(0/2).lrd2 L(1/1).m_lmkval L(1/1).m_lrd d
> y2-dy13 if zero_same==0 & drop==0, dgmmiv(MS, lag(2 5)) dgmmiv(RJVhorMS_s, lag(3 3))  dgmmiv(RJVhorMS_m, lag(3 3))  dgmmiv(RJV
> horMS_l, lag(3 3)) dgmmiv(RJVver, lag(3 3)) dgmmiv(lrd2, lag(2 6))  lgmmiv(l(6/6).MS l(6/6).lrd2)  div(l(1/1).patents_t_2 L(2/
> 2).ta) div(L(1/2).m_lmkval L(1/2).m_lrd dy2-dy13) twostep
note: dy2 dropped from div() because of collinearity
note: dy2 dropped because of collinearity
note: D.dy3 dropped because of collinearity

Dynamic panel-data estimation                Number of obs         =     36485
Group variable: firmnum                      Number of groups      =      5785
Time variable: year
                                             Obs per group:    min =         1
                                                               avg =  6.306828
                                                               max =        12

Number of instruments =    164               Wald chi2(28)         =   5992.86
                                             Prob > chi2           =    0.0000
Two-step results
------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          MS |
         L1. |   .9079929    .015005    60.51   0.000     .8785836    .9374022
             |
  RJVhorMS_s |
         --. |  -.0016401   .0091941    -0.18   0.858    -.0196602    .0163799
         L1. |  -.0030966   .0090969    -0.34   0.734    -.0209262    .0147329
         L2. |  -.0031971   .0061281    -0.52   0.602    -.0152079    .0088136
             |
  RJVhorMS_m |
         --. |   .0036467   .0075339     0.48   0.628    -.0111196    .0184129
         L1. |  -.0133479   .0102172    -1.31   0.191    -.0333733    .0066774
         L2. |   .0001843   .0087899     0.02   0.983    -.0170435    .0174121
             |
  RJVhorMS_l |
         --. |   .0040807   .0082865     0.49   0.622    -.0121605    .0203219
         L1. |  -.0208149   .0100545    -2.07   0.038    -.0405215   -.0011084
         L2. |   .0003711   .0089665     0.04   0.967    -.0172028    .0179451
             |
      RJVver |
         --. |   .0481056   .0122517     3.93   0.000     .0240927    .0721186
         L1. |   .0281713    .011282     2.50   0.013     .0060591    .0502835
         L2. |   -.030528   .0095037    -3.21   0.001    -.0491549   -.0119012
             |
        lrd2 |
         --. |   .0027983   .0013282     2.11   0.035     .0001951    .0054015
         L1. |  -.0017604   .0012883    -1.37   0.172    -.0042855    .0007647
         L2. |  -.0005706   .0002914    -1.96   0.050    -.0011418    5.32e-07
             |
    m_lmkval |
         L1. |  -.0004192   .0004008    -1.05   0.296    -.0012048    .0003663
             |
       m_lrd |
         L1. |   .0027936   .0017894     1.56   0.118    -.0007136    .0063007
             |
         dy4 |  -.0002269   .0002461    -0.92   0.356    -.0007092    .0002553
         dy5 |  -.0004204    .000246    -1.71   0.088    -.0009025    .0000618
         dy6 |  -.0002822   .0002902    -0.97   0.331     -.000851    .0002866
         dy7 |  -.0002817   .0002661    -1.06   0.290    -.0008033    .0002399
         dy8 |  -.0005539   .0002643    -2.10   0.036     -.001072   -.0000358
         dy9 |  -.0003647   .0002986    -1.22   0.222    -.0009499    .0002206
        dy10 |  -.0007468   .0002789    -2.68   0.007    -.0012935   -.0002002
        dy11 |  -.0003707    .000283    -1.31   0.190    -.0009254    .0001839
        dy12 |  -.0004243   .0002563    -1.66   0.098    -.0009266    .0000781
        dy13 |  -.0006283   .0002733    -2.30   0.022     -.001164   -.0000927
       _cons |   .0008488   .0017994     0.47   0.637     -.002678    .0043757
------------------------------------------------------------------------------
Warning: gmm two-step standard errors are biased; robust standard 
         errors are recommended.
Instruments for differenced equation
        GMM-type: L(2/5).MS L(3/3).RJVhorMS_s L(3/3).RJVhorMS_m
                  L(3/3).RJVhorMS_l L(3/3).RJVver L(2/6).lrd2
        Standard: LD.patents_t_2 L2D.ta LD.m_lmkval L2D.m_lmkval LD.m_lrd
                  L2D.m_lrd D.dy3 D.dy4 D.dy5 D.dy6 D.dy7 D.dy8 D.dy9 D.dy10
                  D.dy11 D.dy12 D.dy13
Instruments for level equation
        GMM-type: L7D.MS L7D.lrd2
        Standard: _cons

. estat sargan
Sargan test of overidentifying restrictions
        H0: overidentifying restrictions are valid

        chi2(135)    =  126.1448
        Prob > chi2  =    0.6951

. *no system
. xtdpd L(0/1).MS L(0/2).RJVhorMS_s L(0/2).RJVhorMS_m L(0/2).RJVhorMS_l L(0/2).RJVver L(0/2).lrd2 L(1/1).m_lmkval L(1/1).m_lrd d
> y2-dy13 if zero_same==0 & drop==0, dgmmiv(MS, lag(2 5)) dgmmiv(RJVhorMS_s, lag(3 3))  dgmmiv(RJVhorMS_m, lag(3 3))  dgmmiv(RJV
> horMS_l, lag(3 3)) dgmmiv(RJVver, lag(3 3)) dgmmiv(lrd2, lag(2 6)) div(l(1/1).patents_t_2 L(2/2).ta) div(L(1/2).m_lmkval L(1/2
> ).m_lrd dy2-dy13) twostep
note: dy2 dropped from div() because of collinearity
note: dy2 dropped because of collinearity
note: D.dy3 dropped because of collinearity

Dynamic panel-data estimation                Number of obs         =     36485
Group variable: firmnum                      Number of groups      =      5785
Time variable: year
                                             Obs per group:    min =         1
                                                               avg =  6.306828
                                                               max =        12

Number of instruments =    152               Wald chi2(28)         =   2127.76
                                             Prob > chi2           =    0.0000
Two-step results
------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          MS |
         L1. |   .8421522   .0216294    38.94   0.000     .7997594    .8845451
             |
  RJVhorMS_s |
         --. |  -.0010921   .0094959    -0.12   0.908    -.0197036    .0175195
         L1. |  -.0065359   .0095765    -0.68   0.495    -.0253055    .0122337
         L2. |  -.0019061   .0065793    -0.29   0.772    -.0148013    .0109891
             |
  RJVhorMS_m |
         --. |   .0048171   .0076843     0.63   0.531    -.0102439    .0198781
         L1. |  -.0175266   .0103889    -1.69   0.092    -.0378885    .0028353
         L2. |  -.0004721   .0089493    -0.05   0.958    -.0180123    .0170681
             |
  RJVhorMS_l |
         --. |   .0068061   .0082678     0.82   0.410    -.0093986    .0230108
         L1. |  -.0231754   .0104591    -2.22   0.027    -.0436748    -.002676
         L2. |  -.0002268   .0091704    -0.02   0.980    -.0182006    .0177469
             |
      RJVver |
         --. |   .0415807    .012499     3.33   0.001      .017083    .0660783
         L1. |    .024238   .0118893     2.04   0.041     .0009354    .0475405
         L2. |  -.0266349   .0096302    -2.77   0.006    -.0455097     -.00776
             |
        lrd2 |
         --. |   .0033903   .0020519     1.65   0.098    -.0006314    .0074121
         L1. |  -.0015979   .0013217    -1.21   0.227    -.0041884    .0009926
         L2. |  -.0005402   .0002979    -1.81   0.070     -.001124    .0000436
             |
    m_lmkval |
         L1. |  -.0002359   .0004326    -0.55   0.585    -.0010838    .0006119
             |
       m_lrd |
         L1. |   .0027149   .0020105     1.35   0.177    -.0012257    .0066554
             |
         dy4 |  -.0001707    .000246    -0.69   0.488    -.0006529    .0003115
         dy5 |  -.0003264   .0002533    -1.29   0.197    -.0008228      .00017
         dy6 |  -.0001743   .0002939    -0.59   0.553    -.0007504    .0004017
         dy7 |  -.0002623   .0002702    -0.97   0.332     -.000792    .0002673
         dy8 |  -.0005589   .0002722    -2.05   0.040    -.0010924   -.0000253
         dy9 |  -.0003471   .0003087    -1.12   0.261    -.0009522     .000258
        dy10 |  -.0006719   .0002769    -2.43   0.015    -.0012146   -.0001291
        dy11 |  -.0003314   .0002898    -1.14   0.253    -.0008993    .0002365
        dy12 |  -.0003821   .0002578    -1.48   0.138    -.0008874    .0001232
        dy13 |  -.0005559   .0002729    -2.04   0.042    -.0010907   -.0000211
       _cons |   .0010426   .0019652     0.53   0.596     -.002809    .0048943
------------------------------------------------------------------------------
Warning: gmm two-step standard errors are biased; robust standard 
         errors are recommended.
Instruments for differenced equation
        GMM-type: L(2/5).MS L(3/3).RJVhorMS_s L(3/3).RJVhorMS_m
                  L(3/3).RJVhorMS_l L(3/3).RJVver L(2/6).lrd2
        Standard: LD.patents_t_2 L2D.ta LD.m_lmkval L2D.m_lmkval LD.m_lrd
                  L2D.m_lrd D.dy3 D.dy4 D.dy5 D.dy6 D.dy7 D.dy8 D.dy9 D.dy10
                  D.dy11 D.dy12 D.dy13
Instruments for level equation
        Standard: _cons

. estat sargan
Sargan test of overidentifying restrictions
        H0: overidentifying restrictions are valid

        chi2(123)    =  118.7219
        Prob > chi2  =    0.5923

. xtdpd L(0/1).MS L(0/2).RJVhorMS_s L(0/2).RJVhorMS_m L(0/2).RJVhorMS_l L(0/2).RJVver L(0/2).lrd2 L(1/1).m_lmkval L(1/1).m_lrd d
> y2-dy13 if zero_same==0 & drop==0, dgmmiv(MS, lag(2 5)) dgmmiv(RJVhorMS_s, lag(3 3))  dgmmiv(RJVhorMS_m, lag(3 3))  dgmmiv(RJV
> horMS_l, lag(3 3)) dgmmiv(RJVver, lag(3 3)) dgmmiv(lrd2, lag(2 6))  lgmmiv(l(6/6).MS l(6/6).lrd2)  div(l(1/1).patents_t_2 L(2/
> 2).ta) div(L(1/2).m_lmkval L(1/2).m_lrd dy2-dy13) twostep vce(r)
note: dy2 dropped from div() because of collinearity
note: dy2 dropped because of collinearity
note: D.dy3 dropped because of collinearity

Dynamic panel-data estimation                Number of obs         =     36485
Group variable: firmnum                      Number of groups      =      5785
Time variable: year
                                             Obs per group:    min =         1
                                                               avg =  6.306828
                                                               max =        12

Number of instruments =    164               Wald chi2(28)         =   1090.36
                                             Prob > chi2           =    0.0000
Two-step results
                                (Std. Err. adjusted for clustering on firmnum)
------------------------------------------------------------------------------
             |              WC-Robust
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          MS |
         L1. |   .9079929   .0451262    20.12   0.000     .8195472    .9964386
             |
  RJVhorMS_s |
         --. |  -.0016401    .013807    -0.12   0.905    -.0287014    .0254211
         L1. |  -.0030966   .0177946    -0.17   0.862    -.0379734    .0317801
         L2. |  -.0031971   .0110513    -0.29   0.772    -.0248573    .0184631
             |
  RJVhorMS_m |
         --. |   .0036467   .0111013     0.33   0.743    -.0181114    .0254048
         L1. |  -.0133479   .0152121    -0.88   0.380     -.043163    .0164672
         L2. |   .0001843   .0113027     0.02   0.987    -.0219686    .0223372
             |
  RJVhorMS_l |
         --. |   .0040807   .0132803     0.31   0.759    -.0219483    .0301097
         L1. |  -.0208149   .0145568    -1.43   0.153    -.0493457    .0077158
         L2. |   .0003711    .011879     0.03   0.975    -.0229113    .0236535
             |
      RJVver |
         --. |   .0481056    .022276     2.16   0.031     .0044454    .0917658
         L1. |   .0281713   .0167208     1.68   0.092    -.0046009    .0609435
         L2. |   -.030528   .0153312    -1.99   0.046    -.0605767   -.0004794
             |
        lrd2 |
         --. |   .0027983   .0019137     1.46   0.144    -.0009526    .0065491
         L1. |  -.0017604   .0020759    -0.85   0.396    -.0058291    .0023084
         L2. |  -.0005706   .0003257    -1.75   0.080    -.0012089    .0000677
             |
    m_lmkval |
         L1. |  -.0004192   .0004767    -0.88   0.379    -.0013535     .000515
             |
       m_lrd |
         L1. |   .0027936    .002387     1.17   0.242    -.0018849     .007472
             |
         dy4 |  -.0002269    .000274    -0.83   0.408    -.0007639      .00031
         dy5 |  -.0004204      .0003    -1.40   0.161    -.0010083    .0001676
         dy6 |  -.0002822   .0003152    -0.90   0.371       -.0009    .0003356
         dy7 |  -.0002817   .0003081    -0.91   0.361    -.0008856    .0003221
         dy8 |  -.0005539   .0002895    -1.91   0.056    -.0011214    .0000136
         dy9 |  -.0003647   .0003806    -0.96   0.338    -.0011106    .0003813
        dy10 |  -.0007468   .0003383    -2.21   0.027    -.0014099   -.0000838
        dy11 |  -.0003707   .0003485    -1.06   0.287    -.0010537    .0003122
        dy12 |  -.0004243   .0002997    -1.42   0.157    -.0010117    .0001632
        dy13 |  -.0006283   .0003525    -1.78   0.075    -.0013192    .0000625
       _cons |   .0008488   .0025089     0.34   0.735    -.0040685    .0057662
------------------------------------------------------------------------------
Instruments for differenced equation
        GMM-type: L(2/5).MS L(3/3).RJVhorMS_s L(3/3).RJVhorMS_m
                  L(3/3).RJVhorMS_l L(3/3).RJVver L(2/6).lrd2
        Standard: LD.patents_t_2 L2D.ta LD.m_lmkval L2D.m_lmkval LD.m_lrd
                  L2D.m_lrd D.dy3 D.dy4 D.dy5 D.dy6 D.dy7 D.dy8 D.dy9 D.dy10
                  D.dy11 D.dy12 D.dy13
Instruments for level equation
        GMM-type: L7D.MS L7D.lrd2
        Standard: _cons

. estat abond

Arellano-Bond test for zero autocorrelation in first-differenced errors
  +-----------------------+
  |Order |  z     Prob > z|
  |------+----------------|
  |   1  |-4.8837  0.0000 |
  |   2  | .54147  0.5882 |
  +-----------------------+
   H0: no autocorrelation 

. eststo sml_ms

. lincom RJVhorMS_s+L1.RJVhorMS_s+l2.RJVhorMS_s

 ( 1)  RJVhorMS_s + L.RJVhorMS_s + L2.RJVhorMS_s = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0079339   .0080282    -0.99   0.323    -.0236689    .0078012
------------------------------------------------------------------------------

. lincom RJVhorMS_m+L1.RJVhorMS_m+l2.RJVhorMS_m

 ( 1)  RJVhorMS_m + L.RJVhorMS_m + L2.RJVhorMS_m = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   -.009517   .0069742    -1.36   0.172    -.0231861    .0041522
------------------------------------------------------------------------------

. lincom RJVhorMS_l+L1.RJVhorMS_l+l2.RJVhorMS_l

 ( 1)  RJVhorMS_l + L.RJVhorMS_l + L2.RJVhorMS_l = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0163631   .0098635    -1.66   0.097    -.0356951    .0029689
------------------------------------------------------------------------------

. lincom RJVver+L1.RJVver+l2.RJVver

 ( 1)  RJVver + L.RJVver + L2.RJVver = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0457489   .0201076     2.28   0.023     .0063387     .085159
------------------------------------------------------------------------------

. lincom lrd2+L1.lrd2+l2.lrd2

 ( 1)  lrd2 + L.lrd2 + L2.lrd2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0004673   .0011575     0.40   0.686    -.0018013    .0027359
------------------------------------------------------------------------------

. 
. displ chi2(13,11)
.38918238

. 
. ******
. *** VERTICAL SMALL - MEDIUM - LARGE - Table 5
. ******
. 
. gen RJVver_s=0

. replace  RJVver_s= RJVver if links2_ver<=7
(778 real changes made)

. gen RJVver_m=0

. replace  RJVver_m= RJVver if links2_ver>7 & links2_ver<=43
(1073 real changes made)

. gen RJVver_l=0

. replace  RJVver_l= RJVver if links2_ver>43
(515 real changes made)

. 
. 
. xtdpd L(0/1).MS L(0/2).RJVhor L(0/2).RJVver_s L(0/2).RJVver_m L(0/2).RJVver_l L(0/2).lrd2 L(1/1).m_lmkval L(1/1).m_lrd dy2-dy1
> 3 if zero_same==0 & drop==0, dgmmiv(MS, lag(2 5)) dgmmiv(RJVhor, lag(3 3)) dgmmiv(RJVver_s, lag(3 3))  dgmmiv(RJVver_m, lag(3 
> 3))  dgmmiv(RJVver_l, lag(3 3)) dgmmiv(lrd2, lag(2 6))  lgmmiv(l(6/6).MS l(6/6).lrd2)  div(l(1/1).patents_t_2 L(2/2).ta) div(L
> (1/2).m_lmkval L(1/2).m_lrd dy2-dy13) twostep
note: dy2 dropped from div() because of collinearity
note: dy2 dropped because of collinearity
note: D.dy3 dropped because of collinearity

Dynamic panel-data estimation                Number of obs         =     36485
Group variable: firmnum                      Number of groups      =      5785
Time variable: year
                                             Obs per group:    min =         1
                                                               avg =  6.306828
                                                               max =        12

Number of instruments =    164               Wald chi2(28)         =   5992.24
                                             Prob > chi2           =    0.0000
Two-step results
------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          MS |
         L1. |   .9212481   .0143773    64.08   0.000     .8930691    .9494271
             |
      RJVhor |
         --. |   .0155341   .0099655     1.56   0.119     -.003998    .0350661
         L1. |  -.0294877   .0114383    -2.58   0.010    -.0519064    -.007069
         L2. |   .0043332   .0089814     0.48   0.629    -.0132699    .0219364
             |
    RJVver_s |
         --. |   .0368846   .0231237     1.60   0.111     -.008437    .0822062
         L1. |  -.0506216    .018669    -2.71   0.007    -.0872122    -.014031
         L2. |   .0097998    .013399     0.73   0.465    -.0164618    .0360614
             |
    RJVver_m |
         --. |   .0456769   .0152742     2.99   0.003     .0157401    .0756138
         L1. |   .0140259   .0141361     0.99   0.321    -.0136804    .0417322
         L2. |   -.022562   .0099697    -2.26   0.024    -.0421022   -.0030219
             |
    RJVver_l |
         --. |   .0579875   .0135052     4.29   0.000     .0315179    .0844572
         L1. |   .0116812   .0132818     0.88   0.379    -.0143507    .0377131
         L2. |  -.0220363   .0111671    -1.97   0.048    -.0439235   -.0001492
             |
        lrd2 |
         --. |   .0036253   .0015575     2.33   0.020     .0005726    .0066781
         L1. |  -.0028168   .0015382    -1.83   0.067    -.0058316    .0001981
         L2. |  -.0006085   .0003501    -1.74   0.082    -.0012948    .0000777
             |
    m_lmkval |
         L1. |  -.0002582    .000419    -0.62   0.538    -.0010795     .000563
             |
       m_lrd |
         L1. |   .0036742    .001808     2.03   0.042     .0001307    .0072178
             |
         dy4 |  -.0000249   .0002907    -0.09   0.932    -.0005945    .0005448
         dy5 |  -.0003819   .0002957    -1.29   0.196    -.0009614    .0001976
         dy6 |  -.0002544   .0003303    -0.77   0.441    -.0009018    .0003929
         dy7 |  -.0001443   .0003016    -0.48   0.632    -.0007355    .0004468
         dy8 |  -.0005395   .0003223    -1.67   0.094    -.0011712    .0000923
         dy9 |  -.0001778   .0003358    -0.53   0.597    -.0008359    .0004804
        dy10 |  -.0004618   .0003208    -1.44   0.150    -.0010906    .0001669
        dy11 |  -.0000311   .0003149    -0.10   0.921    -.0006483    .0005861
        dy12 |  -.0001572   .0002917    -0.54   0.590    -.0007289    .0004146
        dy13 |  -.0003367   .0003239    -1.04   0.299    -.0009716    .0002982
       _cons |  -.0004541   .0018058    -0.25   0.801    -.0039933    .0030852
------------------------------------------------------------------------------
Warning: gmm two-step standard errors are biased; robust standard 
         errors are recommended.
Instruments for differenced equation
        GMM-type: L(2/5).MS L(3/3).RJVhor L(3/3).RJVver_s L(3/3).RJVver_m
                  L(3/3).RJVver_l L(2/6).lrd2
        Standard: LD.patents_t_2 L2D.ta LD.m_lmkval L2D.m_lmkval LD.m_lrd
                  L2D.m_lrd D.dy3 D.dy4 D.dy5 D.dy6 D.dy7 D.dy8 D.dy9 D.dy10
                  D.dy11 D.dy12 D.dy13
Instruments for level equation
        GMM-type: L7D.MS L7D.lrd2
        Standard: _cons

. estat sargan
Sargan test of overidentifying restrictions
        H0: overidentifying restrictions are valid

        chi2(135)    =  116.3336
        Prob > chi2  =    0.8753

. *nosystem
. xtdpd L(0/1).MS L(0/2).RJVhor L(0/2).RJVver_s L(0/2).RJVver_m L(0/2).RJVver_l L(0/2).lrd2 L(1/1).m_lmkval L(1/1).m_lrd dy2-dy1
> 3 if zero_same==0 & drop==0, dgmmiv(MS, lag(2 5)) dgmmiv(RJVhor, lag(3 3)) dgmmiv(RJVver_s, lag(3 3))  dgmmiv(RJVver_m, lag(3 
> 3))  dgmmiv(RJVver_l, lag(3 3)) dgmmiv(lrd2, lag(2 6))  div(l(1/1).patents_t_2 L(2/2).ta) div(L(1/2).m_lmkval L(1/2).m_lrd dy2
> -dy13) twostep
note: dy2 dropped from div() because of collinearity
note: dy2 dropped because of collinearity
note: D.dy3 dropped because of collinearity

Dynamic panel-data estimation                Number of obs         =     36485
Group variable: firmnum                      Number of groups      =      5785
Time variable: year
                                             Obs per group:    min =         1
                                                               avg =  6.306828
                                                               max =        12

Number of instruments =    152               Wald chi2(28)         =   2085.43
                                             Prob > chi2           =    0.0000
Two-step results
------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          MS |
         L1. |   .8219608   .0228296    36.00   0.000     .7772157    .8667059
             |
      RJVhor |
         --. |   .0112299   .0100586     1.12   0.264    -.0084845    .0309444
         L1. |  -.0280047   .0114597    -2.44   0.015    -.0504652   -.0055441
         L2. |   .0033171   .0091411     0.36   0.717     -.014599    .0212333
             |
    RJVver_s |
         --. |   .0191189   .0194571     0.98   0.326    -.0190162    .0572541
         L1. |  -.0543444   .0178418    -3.05   0.002    -.0893137    -.019375
         L2. |    .013926   .0121964     1.14   0.254    -.0099786    .0378305
             |
    RJVver_m |
         --. |   .0408691   .0159649     2.56   0.010     .0095784    .0721597
         L1. |   .0119304   .0141293     0.84   0.398    -.0157626    .0396233
         L2. |  -.0187533   .0102662    -1.83   0.068    -.0388748    .0013681
             |
    RJVver_l |
         --. |   .0544362   .0137869     3.95   0.000     .0274143     .081458
         L1. |   .0106609   .0134295     0.79   0.427    -.0156605    .0369822
         L2. |  -.0174889    .011798    -1.48   0.138    -.0406125    .0056348
             |
        lrd2 |
         --. |   .0040522   .0024203     1.67   0.094    -.0006914    .0087958
         L1. |  -.0022516   .0014983    -1.50   0.133    -.0051882     .000685
         L2. |   -.000475   .0003472    -1.37   0.171    -.0011555    .0002055
             |
    m_lmkval |
         L1. |   -.000042   .0004411    -0.10   0.924    -.0009065    .0008225
             |
       m_lrd |
         L1. |   .0038547   .0019934     1.93   0.053    -.0000523    .0077617
             |
         dy4 |   .0000409   .0002818     0.15   0.885    -.0005114    .0005931
         dy5 |  -.0002783   .0002936    -0.95   0.343    -.0008537    .0002971
         dy6 |   -.000129   .0003205    -0.40   0.687    -.0007573    .0004992
         dy7 |  -.0001698   .0003047    -0.56   0.577    -.0007669    .0004274
         dy8 |  -.0005755   .0003214    -1.79   0.073    -.0012055    .0000544
         dy9 |  -.0002385   .0003369    -0.71   0.479    -.0008989    .0004219
        dy10 |  -.0004435   .0003102    -1.43   0.153    -.0010514    .0001645
        dy11 |  -.0000302   .0003136    -0.10   0.923    -.0006449    .0005845
        dy12 |  -.0001427   .0002879    -0.50   0.620     -.000707    .0004217
        dy13 |  -.0003742    .000318    -1.18   0.239    -.0009974     .000249
       _cons |   .0000867   .0018878     0.05   0.963    -.0036134    .0037867
------------------------------------------------------------------------------
Warning: gmm two-step standard errors are biased; robust standard 
         errors are recommended.
Instruments for differenced equation
        GMM-type: L(2/5).MS L(3/3).RJVhor L(3/3).RJVver_s L(3/3).RJVver_m
                  L(3/3).RJVver_l L(2/6).lrd2
        Standard: LD.patents_t_2 L2D.ta LD.m_lmkval L2D.m_lmkval LD.m_lrd
                  L2D.m_lrd D.dy3 D.dy4 D.dy5 D.dy6 D.dy7 D.dy8 D.dy9 D.dy10
                  D.dy11 D.dy12 D.dy13
Instruments for level equation
        Standard: _cons

. estat sargan
Sargan test of overidentifying restrictions
        H0: overidentifying restrictions are valid

        chi2(123)    =    111.21
        Prob > chi2  =    0.7686

. xtdpd L(0/1).MS L(0/2).RJVhor L(0/2).RJVver_s L(0/2).RJVver_m L(0/2).RJVver_l L(0/2).lrd2 L(1/1).m_lmkval L(1/1).m_lrd dy2-dy1
> 3 if zero_same==0 & drop==0, dgmmiv(MS, lag(2 5)) dgmmiv(RJVhor, lag(3 3)) dgmmiv(RJVver_s, lag(3 3))  dgmmiv(RJVver_m, lag(3 
> 3))  dgmmiv(RJVver_l, lag(3 3)) dgmmiv(lrd2, lag(2 6))  lgmmiv(l(6/6).MS l(6/6).lrd2)  div(l(1/1).patents_t_2 L(2/2).ta) div(L
> (1/2).m_lmkval L(1/2).m_lrd dy2-dy13) twostep vce(r)
note: dy2 dropped from div() because of collinearity
note: dy2 dropped because of collinearity
note: D.dy3 dropped because of collinearity

Dynamic panel-data estimation                Number of obs         =     36485
Group variable: firmnum                      Number of groups      =      5785
Time variable: year
                                             Obs per group:    min =         1
                                                               avg =  6.306828
                                                               max =        12

Number of instruments =    164               Wald chi2(28)         =    948.01
                                             Prob > chi2           =    0.0000
Two-step results
                                (Std. Err. adjusted for clustering on firmnum)
------------------------------------------------------------------------------
             |              WC-Robust
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          MS |
         L1. |   .9212481   .0424819    21.69   0.000     .8379852    1.004511
             |
      RJVhor |
         --. |   .0155341   .0157691     0.99   0.325    -.0153728     .046441
         L1. |  -.0294877   .0237314    -1.24   0.214    -.0760005     .017025
         L2. |   .0043332   .0136943     0.32   0.752    -.0225072    .0311736
             |
    RJVver_s |
         --. |   .0368846   .0357536     1.03   0.302    -.0331912    .1069603
         L1. |  -.0506216   .0370888    -1.36   0.172    -.1233143    .0220711
         L2. |   .0097998   .0211113     0.46   0.643    -.0315776    .0511772
             |
    RJVver_m |
         --. |   .0456769   .0225263     2.03   0.043     .0015263    .0898276
         L1. |   .0140259   .0224104     0.63   0.531    -.0298977    .0579495
         L2. |   -.022562   .0140983    -1.60   0.110    -.0501942    .0050701
             |
    RJVver_l |
         --. |   .0579875   .0281067     2.06   0.039     .0028994    .1130756
         L1. |   .0116812   .0197263     0.59   0.554    -.0269816     .050344
         L2. |  -.0220363   .0185234    -1.19   0.234    -.0583415    .0142688
             |
        lrd2 |
         --. |   .0036253   .0018009     2.01   0.044     .0000956    .0071551
         L1. |  -.0028168   .0019336    -1.46   0.145    -.0066066     .000973
         L2. |  -.0006085   .0003851    -1.58   0.114    -.0013634    .0001463
             |
    m_lmkval |
         L1. |  -.0002582   .0004874    -0.53   0.596    -.0012135     .000697
             |
       m_lrd |
         L1. |   .0036742   .0023372     1.57   0.116    -.0009065    .0082549
             |
         dy4 |  -.0000249   .0003772    -0.07   0.947    -.0007642    .0007145
         dy5 |  -.0003819   .0003896    -0.98   0.327    -.0011455    .0003817
         dy6 |  -.0002544   .0004132    -0.62   0.538    -.0010642    .0005554
         dy7 |  -.0001443   .0003875    -0.37   0.710    -.0009038    .0006152
         dy8 |  -.0005395   .0003607    -1.50   0.135    -.0012464    .0001675
         dy9 |  -.0001778   .0004853    -0.37   0.714    -.0011289    .0007734
        dy10 |  -.0004618   .0004252    -1.09   0.277    -.0012953    .0003716
        dy11 |  -.0000311    .000407    -0.08   0.939    -.0008287    .0007666
        dy12 |  -.0001572   .0003641    -0.43   0.666    -.0008708    .0005564
        dy13 |  -.0003367   .0004618    -0.73   0.466    -.0012418    .0005683
       _cons |  -.0004541   .0022162    -0.20   0.838    -.0047978    .0038896
------------------------------------------------------------------------------
Instruments for differenced equation
        GMM-type: L(2/5).MS L(3/3).RJVhor L(3/3).RJVver_s L(3/3).RJVver_m
                  L(3/3).RJVver_l L(2/6).lrd2
        Standard: LD.patents_t_2 L2D.ta LD.m_lmkval L2D.m_lmkval LD.m_lrd
                  L2D.m_lrd D.dy3 D.dy4 D.dy5 D.dy6 D.dy7 D.dy8 D.dy9 D.dy10
                  D.dy11 D.dy12 D.dy13
Instruments for level equation
        GMM-type: L7D.MS L7D.lrd2
        Standard: _cons

. estat abond

Arellano-Bond test for zero autocorrelation in first-differenced errors
  +-----------------------+
  |Order |  z     Prob > z|
  |------+----------------|
  |   1  |-5.1977  0.0000 |
  |   2  | 1.0429  0.2970 |
  +-----------------------+
   H0: no autocorrelation 

. eststo ver_sml

. lincom RJVhor+L1.RJVhor+l2.RJVhor

 ( 1)  RJVhor + L.RJVhor + L2.RJVhor = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0096205   .0055395    -1.74   0.082    -.0204777    .0012368
------------------------------------------------------------------------------

. lincom RJVver_s+L1.RJVver_s+l2.RJVver_s

 ( 1)  RJVver_s + L.RJVver_s + L2.RJVver_s = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0039372   .0302787    -0.13   0.897    -.0632823     .055408
------------------------------------------------------------------------------

. lincom RJVver_m+L1.RJVver_m+l2.RJVver_m

 ( 1)  RJVver_m + L.RJVver_m + L2.RJVver_m = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0371408   .0186403     1.99   0.046     .0006065    .0736752
------------------------------------------------------------------------------

. lincom RJVver_l+L1.RJVver_l+l2.RJVver_l

 ( 1)  RJVver_l + L.RJVver_l + L2.RJVver_l = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0476324    .023567     2.02   0.043     .0014419    .0938228
------------------------------------------------------------------------------

. lincom lrd2+L1.lrd2+l2.lrd2

 ( 1)  lrd2 + L.lrd2 + L2.lrd2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |      .0002   .0011222     0.18   0.859    -.0019995    .0023995
------------------------------------------------------------------------------

. 
. displ chi2(12,6.8)
.12945763

. 
. 
. **********************
. ** continuous effect - Figure 1
. *******************
. 
. xtdpd L(0/1).MS L(0/2).network L(0/2).network2 L(0/2).RJVver L(0/2).lrd2 L(1/1).m_lmkval L(1/1).m_lrd dy2-dy13 if zero_same==0
>  & drop==0, dgmmiv(MS, lag(5 6)) dgmmiv(network, lag(5 6))  dgmmiv(network2, lag(5 6))  dgmmiv(RJVver, lag(3 4)) dgmmiv(lrd2, 
> lag(3 4))  lgmmiv(l(4/5).MS l(4/5).lrd2)  div(l(1/2).patents_t_2 L(1/2).ta) div(L(1/1).m_lmkval L(1/1).m_lrd dy2-dy13) twostep
note: dy2 dropped from div() because of collinearity
note: dy2 dropped because of collinearity
note: D.dy3 dropped because of collinearity

Dynamic panel-data estimation                Number of obs         =     36485
Group variable: firmnum                      Number of groups      =      5785
Time variable: year
                                             Obs per group:    min =         1
                                                               avg =  6.306828
                                                               max =        12

Number of instruments =    141               Wald chi2(25)         =  19830.02
                                             Prob > chi2           =    0.0000
Two-step results
------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          MS |
         L1. |   .9329859   .0096632    96.55   0.000     .9140463    .9519254
             |
     network |
         --. |  -.1214188   .0331365    -3.66   0.000    -.1863652   -.0564725
         L1. |   .0024097   .0454763     0.05   0.958    -.0867222    .0915417
         L2. |  -.0453509   .0357694    -1.27   0.205    -.1154577    .0247558
             |
    network2 |
         --. |   .1343233   .0307826     4.36   0.000     .0739905    .1946561
         L1. |   .0816323   .0572234     1.43   0.154    -.0305235    .1937881
         L2. |   -.034753   .0325154    -1.07   0.285     -.098482    .0289761
             |
      RJVver |
         --. |   .0007033   .0110856     0.06   0.949    -.0210241    .0224308
         L1. |   .0074923   .0112637     0.67   0.506    -.0145842    .0295687
         L2. |   -.007256    .006492    -1.12   0.264    -.0199802    .0054681
             |
        lrd2 |
         --. |  -.0121349   .0015445    -7.86   0.000     -.015162   -.0091077
         L1. |   .0145458   .0018664     7.79   0.000     .0108878    .0182037
         L2. |   .0009868      .0013     0.76   0.448    -.0015612    .0035348
             |
    m_lmkval |
         L1. |  -.0000156   .0003802    -0.04   0.967    -.0007607    .0007295
             |
       m_lrd |
         L1. |  -.0058858   .0016623    -3.54   0.000    -.0091437   -.0026278
             |
         dy4 |   .0000137   .0002806     0.05   0.961    -.0005363    .0005638
         dy5 |  -.0002819   .0002418    -1.17   0.244    -.0007558     .000192
         dy6 |   .0000437   .0002849     0.15   0.878    -.0005148    .0006022
         dy7 |   .0002394   .0002585     0.93   0.354    -.0002673    .0007461
         dy8 |  -.0001553   .0002663    -0.58   0.560    -.0006772    .0003666
         dy9 |   .0000829   .0002871     0.29   0.773    -.0004798    .0006455
        dy10 |   7.27e-06   .0002845     0.03   0.980    -.0005503    .0005649
        dy11 |   .0005209   .0002942     1.77   0.077    -.0000557    .0010976
        dy12 |   .0002172   .0002749     0.79   0.430    -.0003217     .000756
        dy13 |  -.0001875   .0002998    -0.63   0.532    -.0007751    .0004002
       _cons |   .0051097   .0017235     2.96   0.003     .0017316    .0084878
------------------------------------------------------------------------------
Warning: gmm two-step standard errors are biased; robust standard 
         errors are recommended.
Instruments for differenced equation
        GMM-type: L(5/6).MS L(5/6).network L(5/6).network2 L(3/4).RJVver
                  L(3/4).lrd2
        Standard: LD.patents_t_2 L2D.patents_t_2 LD.ta L2D.ta LD.m_lmkval
                  LD.m_lrd D.dy3 D.dy4 D.dy5 D.dy6 D.dy7 D.dy8 D.dy9 D.dy10
                  D.dy11 D.dy12 D.dy13
Instruments for level equation
        GMM-type: L5D.MS L6D.MS L5D.lrd2 L6D.lrd2
        Standard: _cons

. estat sargan
Sargan test of overidentifying restrictions
        H0: overidentifying restrictions are valid

        chi2(115)    =  146.4093
        Prob > chi2  =    0.0255

. xtdpd L(0/1).MS L(0/2).network L(0/2).network2 L(0/2).RJVver L(0/2).lrd2 L(1/1).m_lmkval L(1/1).m_lrd dy2-dy13 if zero_same==0
>  & drop==0, dgmmiv(MS, lag(5 6)) dgmmiv(network, lag(5 6))  dgmmiv(network2, lag(5 6))  dgmmiv(RJVver, lag(3 4)) dgmmiv(lrd2, 
> lag(3 4))  lgmmiv(l(4/5).MS l(4/5).lrd2)  div(l(1/2).patents_t_2 L(1/2).ta) div(L(1/1).m_lmkval L(1/1).m_lrd dy2-dy13) twostep
>  vce(r)
note: dy2 dropped from div() because of collinearity
note: dy2 dropped because of collinearity
note: D.dy3 dropped because of collinearity

Dynamic panel-data estimation                Number of obs         =     36485
Group variable: firmnum                      Number of groups      =      5785
Time variable: year
                                             Obs per group:    min =         1
                                                               avg =  6.306828
                                                               max =        12

Number of instruments =    141               Wald chi2(25)         =   1314.93
                                             Prob > chi2           =    0.0000
Two-step results
                                (Std. Err. adjusted for clustering on firmnum)
------------------------------------------------------------------------------
             |              WC-Robust
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          MS |
         L1. |   .9329859   .0500143    18.65   0.000     .8349597    1.031012
             |
     network |
         --. |  -.1214188   .0869518    -1.40   0.163    -.2918412    .0490035
         L1. |   .0024097   .1225393     0.02   0.984    -.2377629    .2425823
         L2. |  -.0453509   .0882371    -0.51   0.607    -.2182925    .1275906
             |
    network2 |
         --. |   .1343233   .1261681     1.06   0.287    -.1129617    .3816082
         L1. |   .0816323   .1992496     0.41   0.682    -.3088897    .4721543
         L2. |   -.034753   .1039149    -0.33   0.738    -.2384224    .1689164
             |
      RJVver |
         --. |   .0007033   .0281092     0.03   0.980    -.0543897    .0557964
         L1. |   .0074923   .0282183     0.27   0.791    -.0478147    .0627992
         L2. |   -.007256   .0139393    -0.52   0.603    -.0345766    .0200646
             |
        lrd2 |
         --. |  -.0121349   .0067348    -1.80   0.072    -.0253349    .0010651
         L1. |   .0145458   .0078385     1.86   0.064    -.0008175     .029909
         L2. |   .0009868   .0043031     0.23   0.819    -.0074471    .0094207
             |
    m_lmkval |
         L1. |  -.0000156   .0004713    -0.03   0.974    -.0009394    .0009082
             |
       m_lrd |
         L1. |  -.0058858   .0039655    -1.48   0.138    -.0136579    .0018864
             |
         dy4 |   .0000137   .0003389     0.04   0.968    -.0006504    .0006779
         dy5 |  -.0002819   .0003115    -0.90   0.366    -.0008925    .0003287
         dy6 |   .0000437   .0003415     0.13   0.898    -.0006257    .0007131
         dy7 |   .0002394   .0003779     0.63   0.526    -.0005013    .0009801
         dy8 |  -.0001553   .0003737    -0.42   0.678    -.0008877    .0005771
         dy9 |   .0000829   .0004053     0.20   0.838    -.0007115    .0008773
        dy10 |   7.27e-06    .000438     0.02   0.987    -.0008512    .0008657
        dy11 |   .0005209   .0004664     1.12   0.264    -.0003932    .0014351
        dy12 |   .0002172   .0004279     0.51   0.612    -.0006215    .0010559
        dy13 |  -.0001875   .0003549    -0.53   0.597    -.0008831    .0005082
       _cons |   .0051097   .0025632     1.99   0.046      .000086    .0101334
------------------------------------------------------------------------------
Instruments for differenced equation
        GMM-type: L(5/6).MS L(5/6).network L(5/6).network2 L(3/4).RJVver
                  L(3/4).lrd2
        Standard: LD.patents_t_2 L2D.patents_t_2 LD.ta L2D.ta LD.m_lmkval
                  LD.m_lrd D.dy3 D.dy4 D.dy5 D.dy6 D.dy7 D.dy8 D.dy9 D.dy10
                  D.dy11 D.dy12 D.dy13
Instruments for level equation
        GMM-type: L5D.MS L6D.MS L5D.lrd2 L6D.lrd2
        Standard: _cons

. estat abond

Arellano-Bond test for zero autocorrelation in first-differenced errors
  +-----------------------+
  |Order |  z     Prob > z|
  |------+----------------|
  |   1  |-4.6602  0.0000 |
  |   2  | 1.3502  0.1769 |
  +-----------------------+
   H0: no autocorrelation 

. gen eff_net=0.001+(_b[network]+_b[L.network]+_b[L2.network])*network+(_b[network2]+_b[L.network2]+_b[L2.network2])*network2

. label var eff_net "Heterogenous effect I"

. 
. twoway (line eff_net network,  xlabel(0 .1 .2 .3 .4 .5 .6 .7 ) ylabel(.01 0 -.01 -.02 -.03 -.04 -.05 -.06 ) xline(0.18) yline(
> -0.0137) yline(-0.0280) lwidth(thick) sort) if network<.7

. 
. *log using "graph_net.log", replace
. forvalues x=0(.01)1 {
  2. lincom (network + L1.network + L2.network)*`x'+(network2 + L1.network2 + L2.network2)*`x'*`x', l(95)
  3. }

 ( 1) = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |          0  (omitted)
------------------------------------------------------------------------------

 ( 1)  .01*network + .01*L.network + .01*L2.network + .0001*network2 + .0001*L.network2 + .0001*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0016255   .0011479    -1.42   0.157    -.0038753    .0006244
------------------------------------------------------------------------------

 ( 1)  .02*network + .02*L.network + .02*L2.network + .0004*network2 + .0004*L.network2 + .0004*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0032147    .002256    -1.42   0.154    -.0076363    .0012069
------------------------------------------------------------------------------

 ( 1)  .03*network + .03*L.network + .03*L2.network + .0009*network2 + .0009*L.network2 + .0009*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0047677   .0033243    -1.43   0.152    -.0112833    .0017479
------------------------------------------------------------------------------

 ( 1)  .04*network + .04*L.network + .04*L2.network + .0016*network2 + .0016*L.network2 + .0016*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0062845   .0043532    -1.44   0.149    -.0148165    .0022476
------------------------------------------------------------------------------

 ( 1)  .05*network + .05*L.network + .05*L2.network + .0025*network2 + .0025*L.network2 + .0025*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   -.007765   .0053426    -1.45   0.146    -.0182362    .0027062
------------------------------------------------------------------------------

 ( 1)  .06*network + .06*L.network + .06*L2.network + .0036*network2 + .0036*L.network2 + .0036*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0092093   .0062927    -1.46   0.143    -.0215428    .0031242
------------------------------------------------------------------------------

 ( 1)  .07*network + .07*L.network + .07*L2.network + .0049*network2 + .0049*L.network2 + .0049*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0106173   .0072038    -1.47   0.141    -.0247365    .0035019
------------------------------------------------------------------------------

 ( 1)  .08*network + .08*L.network + .08*L2.network + .0064*network2 + .0064*L.network2 + .0064*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0119891    .008076    -1.48   0.138    -.0278178    .0038396
------------------------------------------------------------------------------

 ( 1)  .09*network + .09*L.network + .09*L2.network + .0081*network2 + .0081*L.network2 + .0081*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0133247   .0089095    -1.50   0.135     -.030787    .0041376
------------------------------------------------------------------------------

 ( 1)  .1*network + .1*L.network + .1*L2.network + .01*network2 + .01*L.network2 + .01*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   -.014624   .0097045    -1.51   0.132    -.0336445    .0043966
------------------------------------------------------------------------------

 ( 1)  .11*network + .11*L.network + .11*L2.network + .0121*network2 + .0121*L.network2 + .0121*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0158871   .0104613    -1.52   0.129    -.0363909    .0046168
------------------------------------------------------------------------------

 ( 1)  .12*network + .12*L.network + .12*L2.network + .0144*network2 + .0144*L.network2 + .0144*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0171139   .0111802    -1.53   0.126    -.0390266    .0047988
------------------------------------------------------------------------------

 ( 1)  .13*network + .13*L.network + .13*L2.network + .0169*network2 + .0169*L.network2 + .0169*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0183045   .0118613    -1.54   0.123    -.0415521    .0049432
------------------------------------------------------------------------------

 ( 1)  .14*network + .14*L.network + .14*L2.network + .0196*network2 + .0196*L.network2 + .0196*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0194588    .012505    -1.56   0.120    -.0439681    .0050505
------------------------------------------------------------------------------

 ( 1)  .15*network + .15*L.network + .15*L2.network + .0225*network2 + .0225*L.network2 + .0225*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0205769   .0131116    -1.57   0.117    -.0462752    .0051213
------------------------------------------------------------------------------

 ( 1)  .16*network + .16*L.network + .16*L2.network + .0256*network2 + .0256*L.network2 + .0256*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0216588   .0136815    -1.58   0.113    -.0484741    .0051564
------------------------------------------------------------------------------

 ( 1)  .17*network + .17*L.network + .17*L2.network + .0289*network2 + .0289*L.network2 + .0289*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0227045   .0142151    -1.60   0.110    -.0505655    .0051566
------------------------------------------------------------------------------

 ( 1)  .18*network + .18*L.network + .18*L2.network + .0324*network2 + .0324*L.network2 + .0324*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0237138   .0147127    -1.61   0.107    -.0525502    .0051225
------------------------------------------------------------------------------

 ( 1)  .19*network + .19*L.network + .19*L2.network + .0361*network2 + .0361*L.network2 + .0361*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   -.024687   .0151749    -1.63   0.104    -.0544292    .0050552
------------------------------------------------------------------------------

 ( 1)  .2*network + .2*L.network + .2*L2.network + .04*network2 + .04*L.network2 + .04*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0256239   .0156021    -1.64   0.101    -.0562035    .0049557
------------------------------------------------------------------------------

 ( 1)  .21*network + .21*L.network + .21*L2.network + .0441*network2 + .0441*L.network2 + .0441*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0265246    .015995    -1.66   0.097    -.0578741     .004825
------------------------------------------------------------------------------

 ( 1)  .22*network + .22*L.network + .22*L2.network + .0484*network2 + .0484*L.network2 + .0484*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   -.027389    .016354    -1.67   0.094    -.0594422    .0046642
------------------------------------------------------------------------------

 ( 1)  .23*network + .23*L.network + .23*L2.network + .0529*network2 + .0529*L.network2 + .0529*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0282172   .0166799    -1.69   0.091    -.0609092    .0044748
------------------------------------------------------------------------------

 ( 1)  .24*network + .24*L.network + .24*L2.network + .0576*network2 + .0576*L.network2 + .0576*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0290091   .0169734    -1.71   0.087    -.0622764    .0042581
------------------------------------------------------------------------------

 ( 1)  .25*network + .25*L.network + .25*L2.network + .0625*network2 + .0625*L.network2 + .0625*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0297648   .0172353    -1.73   0.084    -.0635455    .0040158
------------------------------------------------------------------------------

 ( 1)  .26*network + .26*L.network + .26*L2.network + .0676*network2 + .0676*L.network2 + .0676*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0304843   .0174665    -1.75   0.081    -.0647181    .0037495
------------------------------------------------------------------------------

 ( 1)  .27*network + .27*L.network + .27*L2.network + .0729*network2 + .0729*L.network2 + .0729*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0311675    .017668    -1.76   0.078    -.0657962    .0034612
------------------------------------------------------------------------------

 ( 1)  .28*network + .28*L.network + .28*L2.network + .0784*network2 + .0784*L.network2 + .0784*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0318145   .0178409    -1.78   0.075     -.066782     .003153
------------------------------------------------------------------------------

 ( 1)  .29*network + .29*L.network + .29*L2.network + .0841*network2 + .0841*L.network2 + .0841*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0324253   .0179863    -1.80   0.071    -.0676778    .0028272
------------------------------------------------------------------------------

 ( 1)  .3*network + .3*L.network + .3*L2.network + .09*network2 + .09*L.network2 + .09*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0329998   .0181056    -1.82   0.068    -.0684861    .0024865
------------------------------------------------------------------------------

 ( 1)  .31*network + .31*L.network + .31*L2.network + .0961*network2 + .0961*L.network2 + .0961*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   -.033538   .0182003    -1.84   0.065    -.0692099    .0021338
------------------------------------------------------------------------------

 ( 1)  .32*network + .32*L.network + .32*L2.network + .1024*network2 + .1024*L.network2 + .1024*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0340401   .0182719    -1.86   0.062    -.0698523    .0017722
------------------------------------------------------------------------------

 ( 1)  .33*network + .33*L.network + .33*L2.network + .1089*network2 + .1089*L.network2 + .1089*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0345059   .0183223    -1.88   0.060    -.0704169    .0014052
------------------------------------------------------------------------------

 ( 1)  .34*network + .34*L.network + .34*L2.network + .1156*network2 + .1156*L.network2 + .1156*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0349354   .0183535    -1.90   0.057    -.0709076    .0010368
------------------------------------------------------------------------------

 ( 1)  .35*network + .35*L.network + .35*L2.network + .1225*network2 + .1225*L.network2 + .1225*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0353287   .0183676    -1.92   0.054    -.0713286    .0006712
------------------------------------------------------------------------------

 ( 1)  .36*network + .36*L.network + .36*L2.network + .1296*network2 + .1296*L.network2 + .1296*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0356858   .0183672    -1.94   0.052    -.0716848    .0003133
------------------------------------------------------------------------------

 ( 1)  .37*network + .37*L.network + .37*L2.network + .1369*network2 + .1369*L.network2 + .1369*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0360066   .0183549    -1.96   0.050    -.0719815   -.0000316
------------------------------------------------------------------------------

 ( 1)  .38*network + .38*L.network + .38*L2.network + .1444*network2 + .1444*L.network2 + .1444*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0362912   .0183336    -1.98   0.048    -.0722244   -.0003579
------------------------------------------------------------------------------

 ( 1)  .39*network + .39*L.network + .39*L2.network + .1521*network2 + .1521*L.network2 + .1521*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0365395   .0183067    -2.00   0.046    -.0724199   -.0006591
------------------------------------------------------------------------------

 ( 1)  .4*network + .4*L.network + .4*L2.network + .16*network2 + .16*L.network2 + .16*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0367516   .0182776    -2.01   0.044     -.072575   -.0009282
------------------------------------------------------------------------------

 ( 1)  .41*network + .41*L.network + .41*L2.network + .1681*network2 + .1681*L.network2 + .1681*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0369275   .0182502    -2.02   0.043    -.0726972   -.0011577
------------------------------------------------------------------------------

 ( 1)  .42*network + .42*L.network + .42*L2.network + .1764*network2 + .1764*L.network2 + .1764*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0370671   .0182288    -2.03   0.042    -.0727948   -.0013394
------------------------------------------------------------------------------

 ( 1)  .43*network + .43*L.network + .43*L2.network + .1849*network2 + .1849*L.network2 + .1849*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0371705   .0182177    -2.04   0.041    -.0728765   -.0014644
------------------------------------------------------------------------------

 ( 1)  .44*network + .44*L.network + .44*L2.network + .1936*network2 + .1936*L.network2 + .1936*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0372376   .0182218    -2.04   0.041    -.0729517   -.0015235
------------------------------------------------------------------------------

 ( 1)  .45*network + .45*L.network + .45*L2.network + .2025*network2 + .2025*L.network2 + .2025*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0372685   .0182461    -2.04   0.041    -.0730302   -.0015068
------------------------------------------------------------------------------

 ( 1)  .46*network + .46*L.network + .46*L2.network + .2116*network2 + .2116*L.network2 + .2116*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0372631   .0182959    -2.04   0.042    -.0731224   -.0014039
------------------------------------------------------------------------------

 ( 1)  .47*network + .47*L.network + .47*L2.network + .2209*network2 + .2209*L.network2 + .2209*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0372216   .0183764    -2.03   0.043    -.0732387   -.0012044
------------------------------------------------------------------------------

 ( 1)  .48*network + .48*L.network + .48*L2.network + .2304*network2 + .2304*L.network2 + .2304*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0371437   .0184933    -2.01   0.045    -.0733899   -.0008976
------------------------------------------------------------------------------

 ( 1)  .49*network + .49*L.network + .49*L2.network + .2401*network2 + .2401*L.network2 + .2401*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0370297   .0186518    -1.99   0.047    -.0735865   -.0004728
------------------------------------------------------------------------------

 ( 1)  .5*network + .5*L.network + .5*L2.network + .25*network2 + .25*L.network2 + .25*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0368794   .0188572    -1.96   0.050    -.0738389    .0000801
------------------------------------------------------------------------------

 ( 1)  .51*network + .51*L.network + .51*L2.network + .2601*network2 + .2601*L.network2 + .2601*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0366928   .0191145    -1.92   0.055    -.0741566     .000771
------------------------------------------------------------------------------

 ( 1)  .52*network + .52*L.network + .52*L2.network + .2704*network2 + .2704*L.network2 + .2704*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |    -.03647   .0194283    -1.88   0.060    -.0745489    .0016088
------------------------------------------------------------------------------

 ( 1)  .53*network + .53*L.network + .53*L2.network + .2809*network2 + .2809*L.network2 + .2809*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   -.036211   .0198027    -1.83   0.067    -.0750236    .0026016
------------------------------------------------------------------------------

 ( 1)  .54*network + .54*L.network + .54*L2.network + .2916*network2 + .2916*L.network2 + .2916*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0359157   .0202413    -1.77   0.076    -.0755879    .0037564
------------------------------------------------------------------------------

 ( 1)  .55*network + .55*L.network + .55*L2.network + .3025*network2 + .3025*L.network2 + .3025*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0355842   .0207469    -1.72   0.086    -.0762475     .005079
------------------------------------------------------------------------------

 ( 1)  .56*network + .56*L.network + .56*L2.network + .3136*network2 + .3136*L.network2 + .3136*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0352165    .021322    -1.65   0.099    -.0770068    .0065738
------------------------------------------------------------------------------

 ( 1)  .57*network + .57*L.network + .57*L2.network + .3249*network2 + .3249*L.network2 + .3249*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0348125    .021968    -1.58   0.113    -.0778691    .0082441
------------------------------------------------------------------------------

 ( 1)  .58*network + .58*L.network + .58*L2.network + .3364*network2 + .3364*L.network2 + .3364*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0343723   .0226862    -1.52   0.130    -.0788364    .0100919
------------------------------------------------------------------------------

 ( 1)  .59*network + .59*L.network + .59*L2.network + .3481*network2 + .3481*L.network2 + .3481*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0338958   .0234769    -1.44   0.149    -.0799097    .0121181
------------------------------------------------------------------------------

 ( 1)  .6*network + .6*L.network + .6*L2.network + .36*network2 + .36*L.network2 + .36*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0333831   .0243402    -1.37   0.170    -.0810891    .0143229
------------------------------------------------------------------------------

 ( 1)  .61*network + .61*L.network + .61*L2.network + .3721*network2 + .3721*L.network2 + .3721*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0328341   .0252757    -1.30   0.194    -.0823736    .0167053
------------------------------------------------------------------------------

 ( 1)  .62*network + .62*L.network + .62*L2.network + .3844*network2 + .3844*L.network2 + .3844*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0322489   .0262825    -1.23   0.220    -.0837618    .0192639
------------------------------------------------------------------------------

 ( 1)  .63*network + .63*L.network + .63*L2.network + .3969*network2 + .3969*L.network2 + .3969*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0316275   .0273598    -1.16   0.248    -.0852516    .0219966
------------------------------------------------------------------------------

 ( 1)  .64*network + .64*L.network + .64*L2.network + .4096*network2 + .4096*L.network2 + .4096*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0309698   .0285062    -1.09   0.277    -.0868409    .0249012
------------------------------------------------------------------------------

 ( 1)  .65*network + .65*L.network + .65*L2.network + .4225*network2 + .4225*L.network2 + .4225*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0302759   .0297205    -1.02   0.308     -.088527    .0279751
------------------------------------------------------------------------------

 ( 1)  .66*network + .66*L.network + .66*L2.network + .4356*network2 + .4356*L.network2 + .4356*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0295458   .0310012    -0.95   0.341     -.090307    .0312155
------------------------------------------------------------------------------

 ( 1)  .67*network + .67*L.network + .67*L2.network + .4489*network2 + .4489*L.network2 + .4489*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0287794    .032347    -0.89   0.374    -.0921784    .0346196
------------------------------------------------------------------------------

 ( 1)  .68*network + .68*L.network + .68*L2.network + .4624*network2 + .4624*L.network2 + .4624*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0279767   .0337565    -0.83   0.407    -.0941382    .0381847
------------------------------------------------------------------------------

 ( 1)  .69*network + .69*L.network + .69*L2.network + .4761*network2 + .4761*L.network2 + .4761*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0271379   .0352281    -0.77   0.441    -.0961838     .041908
------------------------------------------------------------------------------

 ( 1)  .7*network + .7*L.network + .7*L2.network + .49*network2 + .49*L.network2 + .49*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0262628   .0367607    -0.71   0.475    -.0983124    .0457869
------------------------------------------------------------------------------

 ( 1)  .71*network + .71*L.network + .71*L2.network + .5041*network2 + .5041*L.network2 + .5041*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0253514   .0383529    -0.66   0.509    -.1005217    .0498189
------------------------------------------------------------------------------

 ( 1)  .72*network + .72*L.network + .72*L2.network + .5184*network2 + .5184*L.network2 + .5184*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0244038   .0400035    -0.61   0.542    -.1028092    .0540016
------------------------------------------------------------------------------

 ( 1)  .73*network + .73*L.network + .73*L2.network + .5329*network2 + .5329*L.network2 + .5329*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |    -.02342   .0417113    -0.56   0.574    -.1051726    .0583327
------------------------------------------------------------------------------

 ( 1)  .74*network + .74*L.network + .74*L2.network + .5476*network2 + .5476*L.network2 + .5476*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0223999   .0434753    -0.52   0.606    -.1076099    .0628101
------------------------------------------------------------------------------

 ( 1)  .75*network + .75*L.network + .75*L2.network + .5625*network2 + .5625*L.network2 + .5625*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0213436   .0452945    -0.47   0.637    -.1101191    .0674319
------------------------------------------------------------------------------

 ( 1)  .76*network + .76*L.network + .76*L2.network + .5776*network2 + .5776*L.network2 + .5776*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   -.020251   .0471679    -0.43   0.668    -.1126984    .0721963
------------------------------------------------------------------------------

 ( 1)  .77*network + .77*L.network + .77*L2.network + .5929*network2 + .5929*L.network2 + .5929*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0191222   .0490947    -0.39   0.697     -.115346    .0771016
------------------------------------------------------------------------------

 ( 1)  .78*network + .78*L.network + .78*L2.network + .6084*network2 + .6084*L.network2 + .6084*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0179572   .0510741    -0.35   0.725    -.1180606    .0821462
------------------------------------------------------------------------------

 ( 1)  .79*network + .79*L.network + .79*L2.network + .6241*network2 + .6241*L.network2 + .6241*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0167559   .0531054    -0.32   0.752    -.1208405    .0873287
------------------------------------------------------------------------------

 ( 1)  .8*network + .8*L.network + .8*L2.network + .64*network2 + .64*L.network2 + .64*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0155184   .0551878    -0.28   0.779    -.1236845    .0926477
------------------------------------------------------------------------------

 ( 1)  .81*network + .81*L.network + .81*L2.network + .6561*network2 + .6561*L.network2 + .6561*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0142446   .0573208    -0.25   0.804    -.1265913    .0981021
------------------------------------------------------------------------------

 ( 1)  .82*network + .82*L.network + .82*L2.network + .6724*network2 + .6724*L.network2 + .6724*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0129346   .0595038    -0.22   0.828    -.1295599    .1036907
------------------------------------------------------------------------------

 ( 1)  .83*network + .83*L.network + .83*L2.network + .6889*network2 + .6889*L.network2 + .6889*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0115884   .0617362    -0.19   0.851    -.1325892    .1094124
------------------------------------------------------------------------------

 ( 1)  .84*network + .84*L.network + .84*L2.network + .7056*network2 + .7056*L.network2 + .7056*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0102059   .0640177    -0.16   0.873    -.1356782    .1152664
------------------------------------------------------------------------------

 ( 1)  .85*network + .85*L.network + .85*L2.network + .7225*network2 + .7225*L.network2 + .7225*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0087872   .0663476    -0.13   0.895    -.1388261    .1212517
------------------------------------------------------------------------------

 ( 1)  .86*network + .86*L.network + .86*L2.network + .7396*network2 + .7396*L.network2 + .7396*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0073322   .0687256    -0.11   0.915     -.142032    .1273676
------------------------------------------------------------------------------

 ( 1)  .87*network + .87*L.network + .87*L2.network + .7569*network2 + .7569*L.network2 + .7569*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   -.005841   .0711514    -0.08   0.935    -.1452952    .1336132
------------------------------------------------------------------------------

 ( 1)  .88*network + .88*L.network + .88*L2.network + .7744*network2 + .7744*L.network2 + .7744*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0043135   .0736245    -0.06   0.953     -.148615    .1399879
------------------------------------------------------------------------------

 ( 1)  .89*network + .89*L.network + .89*L2.network + .7921*network2 + .7921*L.network2 + .7921*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0027499   .0761447    -0.04   0.971    -.1519908    .1464911
------------------------------------------------------------------------------

 ( 1)  .9*network + .9*L.network + .9*L2.network + .81*network2 + .81*L.network2 + .81*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -.0011499   .0787117    -0.01   0.988     -.155422    .1531221
------------------------------------------------------------------------------

 ( 1)  .91*network + .91*L.network + .91*L2.network + .8281*network2 + .8281*L.network2 + .8281*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0004862   .0813251     0.01   0.995     -.158908    .1598804
------------------------------------------------------------------------------

 ( 1)  .92*network + .92*L.network + .92*L2.network + .8464*network2 + .8464*L.network2 + .8464*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0021586   .0839847     0.03   0.979    -.1624483    .1667656
------------------------------------------------------------------------------

 ( 1)  .93*network + .93*L.network + .93*L2.network + .8649*network2 + .8649*L.network2 + .8649*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0038673   .0866903     0.04   0.964    -.1660425    .1737771
------------------------------------------------------------------------------

 ( 1)  .94*network + .94*L.network + .94*L2.network + .8836*network2 + .8836*L.network2 + .8836*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0056122   .0894416     0.06   0.950    -.1696901    .1809145
------------------------------------------------------------------------------

 ( 1)  .95*network + .95*L.network + .95*L2.network + .9025*network2 + .9025*L.network2 + .9025*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0073933   .0922385     0.08   0.936    -.1733908    .1881774
------------------------------------------------------------------------------

 ( 1)  .96*network + .96*L.network + .96*L2.network + .9216*network2 + .9216*L.network2 + .9216*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0092107   .0950807     0.10   0.923    -.1771441    .1955654
------------------------------------------------------------------------------

 ( 1)  .97*network + .97*L.network + .97*L2.network + .9409*network2 + .9409*L.network2 + .9409*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0110643   .0979681     0.11   0.910    -.1809497    .2030782
------------------------------------------------------------------------------

 ( 1)  .98*network + .98*L.network + .98*L2.network + .9604*network2 + .9604*L.network2 + .9604*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0129541   .1009005     0.13   0.898    -.1848073    .2107155
------------------------------------------------------------------------------

 ( 1)  .99*network + .99*L.network + .99*L2.network + .9801*network2 + .9801*L.network2 + .9801*L2.network2 = 0

------------------------------------------------------------------------------
          MS |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |   .0148802   .1038778     0.14   0.886    -.1887165     .218477
------------------------------------------------------------------------------

. *log close
. 
. 
. *************************
. ** correlation with the Gartner data
. *************************
. 
. 
. **************************
. ** since the Gartner data are proprietary we can not provide it.
. ** we therfore dropped them from our database after perfoming the command
. ** the command is then commented out
. ** the outcome of the command can be seen in the log file
. **************************
. 
.  *here we merge with the gartner data
. so ticker year

. 
. merge ticker year using "DRAM.dta

. egen mean_MS=mean(MS), by(SIC4 year)
(2197 missing values generated)

. egen mean_MSw=mean(MSw), by(SIC4 year)
(2197 missing values generated)

. egen mean_MS=mean(MS), by(SIC4 year)
(2197 missing values generated)

. egen mean_MSw=mean(MSw), by(SIC4 year)
(2197 missing values generated)

. 
. tab SIC4 if msh>0 & MS!=. & _merge==3

primary sic |
       code |      Freq.     Percent        Cum.
------------+-----------------------------------
       3620 |          9        1.53        1.53
       3640 |         11        1.87        3.41
       3663 |         38        6.47        9.88
       3670 |          9        1.53       11.41
       3674 |        518       88.25       99.66
       3679 |          2        0.34      100.00
------------+-----------------------------------
      Total |        587      100.00

. tab year if _merge==3

   calender |
       year |      Freq.     Percent        Cum.
------------+-----------------------------------
       1989 |         40        5.37        5.37
       1990 |         44        5.91       11.28
       1991 |         48        6.44       17.72
       1992 |         56        7.52       25.23
       1993 |         62        8.32       33.56
       1994 |         72        9.66       43.22
       1995 |         77       10.34       53.56
       1996 |         87       11.68       65.23
       1997 |         90       12.08       77.32
       1998 |         88       11.81       89.13
       1999 |         81       10.87      100.00
------------+-----------------------------------
      Total |        745      100.00

. tab year if _merge==3 & msh>0

   calender |
       year |      Freq.     Percent        Cum.
------------+-----------------------------------
       1989 |         31        5.28        5.28
       1990 |         36        6.13       11.41
       1991 |         37        6.30       17.72
       1992 |         47        8.01       25.72
       1993 |         50        8.52       34.24
       1994 |         55        9.37       43.61
       1995 |         59       10.05       53.66
       1996 |         61       10.39       64.05
       1997 |         68       11.58       75.64
       1998 |         77       13.12       88.76
       1999 |         66       11.24      100.00
------------+-----------------------------------
      Total |        587      100.00

. tab year if _merge==3 & msh>0 & MS>0

   calender |
       year |      Freq.     Percent        Cum.
------------+-----------------------------------
       1989 |         31        5.28        5.28
       1990 |         36        6.13       11.41
       1991 |         37        6.30       17.72
       1992 |         47        8.01       25.72
       1993 |         50        8.52       34.24
       1994 |         55        9.37       43.61
       1995 |         59       10.05       53.66
       1996 |         61       10.39       64.05
       1997 |         68       11.58       75.64
       1998 |         77       13.12       88.76
       1999 |         66       11.24      100.00
------------+-----------------------------------
      Total |        587      100.00

. tab year if _merge==3 & msh>0 & MS>0 & MS!=.

   calender |
       year |      Freq.     Percent        Cum.
------------+-----------------------------------
       1989 |         31        5.28        5.28
       1990 |         36        6.13       11.41
       1991 |         37        6.30       17.72
       1992 |         47        8.01       25.72
       1993 |         50        8.52       34.24
       1994 |         55        9.37       43.61
       1995 |         59       10.05       53.66
       1996 |         61       10.39       64.05
       1997 |         68       11.58       75.64
       1998 |         77       13.12       88.76
       1999 |         66       11.24      100.00
------------+-----------------------------------
      Total |        587      100.00

. pwcorr MS msh msh_ma if _merge==3 & msh>0 & MS!=., sig

             |       MS      msh msh_ma~d
-------------+---------------------------
          MS |   1.0000 
             |
             |
         msh |   0.5578   1.0000 
             |   0.0000
             |
 msh_matched |   0.5810   0.9927   1.0000 
             |   0.0000   0.0000
             |

. pwcorr MS msh msh_ma if _merge==3 & msh>0 & MS!=. & SIC4==3674, sig

             |       MS      msh msh_ma~d
-------------+---------------------------
          MS |   1.0000 
             |
             |
         msh |   0.8957   1.0000 
             |   0.0000
             |
 msh_matched |   0.9282   0.9925   1.0000 
             |   0.0000   0.0000
             |

. bysort year: pwcorr MS MSw msh msh_ma if _merge==3 & msh>0 & MS!=. & SIC4==3674, sig

-------------------------------------------------------------------------------------------------
-> year = 1986

             |       MS      MSw      msh msh_ma~d
-------------+------------------------------------
          MS |        . 
             |
             |
         MSw |        .        . 
             |        .
             |
         msh |        .        .        . 
             |        .        .
             |
 msh_matched |        .        .        .        . 
             |        .        .        .
             |

-------------------------------------------------------------------------------------------------
-> year = 1987

             |       MS      MSw      msh msh_ma~d
-------------+------------------------------------
          MS |        . 
             |
             |
         MSw |        .        . 
             |        .
             |
         msh |        .        .        . 
             |        .        .
             |
 msh_matched |        .        .        .        . 
             |        .        .        .
             |

-------------------------------------------------------------------------------------------------
-> year = 1988

             |       MS      MSw      msh msh_ma~d
-------------+------------------------------------
          MS |        . 
             |
             |
         MSw |        .        . 
             |        .
             |
         msh |        .        .        . 
             |        .        .
             |
 msh_matched |        .        .        .        . 
             |        .        .        .
             |

-------------------------------------------------------------------------------------------------
-> year = 1989

             |       MS      MSw      msh msh_ma~d
-------------+------------------------------------
          MS |   1.0000 
             |
             |
         MSw |   0.9840   1.0000 
             |   0.0000
             |
         msh |   0.9379   0.9587   1.0000 
             |   0.0000   0.0000
             |
 msh_matched |   0.9379   0.9587   1.0000   1.0000 
             |   0.0000   0.0000   0.0000
             |

-------------------------------------------------------------------------------------------------
-> year = 1990

             |       MS      MSw      msh msh_ma~d
-------------+------------------------------------
          MS |   1.0000 
             |
             |
         MSw |   0.9846   1.0000 
             |   0.0000
             |
         msh |   0.9155   0.9343   1.0000 
             |   0.0000   0.0000
             |
 msh_matched |   0.9155   0.9343   1.0000   1.0000 
             |   0.0000   0.0000   0.0000
             |

-------------------------------------------------------------------------------------------------
-> year = 1991

             |       MS      MSw      msh msh_ma~d
-------------+------------------------------------
          MS |   1.0000 
             |
             |
         MSw |   0.9850   1.0000 
             |   0.0000
             |
         msh |   0.9200   0.9317   1.0000 
             |   0.0000   0.0000
             |
 msh_matched |   0.9200   0.9317   1.0000   1.0000 
             |   0.0000   0.0000   0.0000
             |

-------------------------------------------------------------------------------------------------
-> year = 1992

             |       MS      MSw      msh msh_ma~d
-------------+------------------------------------
          MS |   1.0000 
             |
             |
         MSw |   0.9864   1.0000 
             |   0.0000
             |
         msh |   0.9228   0.9342   1.0000 
             |   0.0000   0.0000
             |
 msh_matched |   0.9228   0.9342   1.0000   1.0000 
             |   0.0000   0.0000   0.0000
             |

-------------------------------------------------------------------------------------------------
-> year = 1993

             |       MS      MSw      msh msh_ma~d
-------------+------------------------------------
          MS |   1.0000 
             |
             |
         MSw |   0.9887   1.0000 
             |   0.0000
             |
         msh |   0.9496   0.9529   1.0000 
             |   0.0000   0.0000
             |
 msh_matched |   0.9496   0.9529   1.0000   1.0000 
             |   0.0000   0.0000   0.0000
             |

-------------------------------------------------------------------------------------------------
-> year = 1994

             |       MS      MSw      msh msh_ma~d
-------------+------------------------------------
          MS |   1.0000 
             |
             |
         MSw |   0.9886   1.0000 
             |   0.0000
             |
         msh |   0.9709   0.9695   1.0000 
             |   0.0000   0.0000
             |
 msh_matched |   0.9709   0.9695   1.0000   1.0000 
             |   0.0000   0.0000   0.0000
             |

-------------------------------------------------------------------------------------------------
-> year = 1995

             |       MS      MSw      msh msh_ma~d
-------------+------------------------------------
          MS |   1.0000 
             |
             |
         MSw |   0.9898   1.0000 
             |   0.0000
             |
         msh |   0.9868   0.9851   1.0000 
             |   0.0000   0.0000
             |
 msh_matched |   0.9868   0.9851   1.0000   1.0000 
             |   0.0000   0.0000   0.0000
             |

-------------------------------------------------------------------------------------------------
-> year = 1996

             |       MS      MSw      msh msh_ma~d
-------------+------------------------------------
          MS |   1.0000 
             |
             |
         MSw |   0.9900   1.0000 
             |   0.0000
             |
         msh |   0.9937   0.9818   1.0000 
             |   0.0000   0.0000
             |
 msh_matched |   0.9937   0.9818   1.0000   1.0000 
             |   0.0000   0.0000   0.0000
             |

-------------------------------------------------------------------------------------------------
-> year = 1997

             |       MS      MSw      msh msh_ma~d
-------------+------------------------------------
          MS |   1.0000 
             |
             |
         MSw |   0.9920   1.0000 
             |   0.0000
             |
         msh |   0.9965   0.9871   1.0000 
             |   0.0000   0.0000
             |
 msh_matched |   0.9965   0.9871   1.0000   1.0000 
             |   0.0000   0.0000   0.0000
             |

-------------------------------------------------------------------------------------------------
-> year = 1998

             |       MS      MSw      msh msh_ma~d
-------------+------------------------------------
          MS |   1.0000 
             |
             |
         MSw |   0.9930   1.0000 
             |   0.0000
             |
         msh |   0.9969   0.9902   1.0000 
             |   0.0000   0.0000
             |
 msh_matched |   0.9969   0.9902   1.0000   1.0000 
             |   0.0000   0.0000   0.0000
             |

-------------------------------------------------------------------------------------------------
-> year = 1999

             |       MS      MSw      msh msh_ma~d
-------------+------------------------------------
          MS |   1.0000 
             |
             |
         MSw |   0.9842   1.0000 
             |   0.0000
             |
         msh |   0.9920   0.9584   1.0000 
             |   0.0000   0.0000
             |
 msh_matched |   0.9920   0.9584   1.0000   1.0000 
             |   0.0000   0.0000   0.0000
             |

. 
. esttab RJV1p RJV2p RJV3p RJV4p RJV5p RJV6p using  "tables\REStat_MS14767_Vol96(2)_tables", replace label  nodepvar star(* 0.10
>  ** 0.05 *** 0.01) se  stats(N) drop(dy*) compress csv
(output written to tables\REStat_MS14767_Vol96(2)_tables.csv)

. esttab any hor_ver hor_sml using  "tables\REStat_MS14767_Vol96(2)_tables", append nodepvar star(* 0.10 ** 0.05 *** 0.01) se  s
> tats(r N ) drop(dy*) label compress csv nogaps
(output written to tables\REStat_MS14767_Vol96(2)_tables.csv)

. esttab any ver_sml using  "tables\REStat_MS14767_Vol96(2)_tables", append nodepvar star(* 0.10 ** 0.05 *** 0.01) se  stats(r N
>  ) drop(dy*) label compress csv nogaps
(output written to tables\REStat_MS14767_Vol96(2)_tables.csv)

. esttab any sml_ms using  "tables\REStat_MS14767_Vol96(2)_tables", append nodepvar star(* 0.10 ** 0.05 *** 0.01) se  stats(r N 
> ) drop(dy*) label compress csv nogaps
(output written to tables\REStat_MS14767_Vol96(2)_tables.csv)

. 
. log close
      name:  <unnamed>
       log:  E:\REStat_MS14767_Vol96(2)\Estimation\REStat_MS14797_Vol96(2)_estimation_compustat.log
  log type:  text
 closed on:  20 Dec 2014, 21:01:17
--------------------------------------------------------------------------------------------------------------------------------
